Big Data

A High Performance Platform For The Full Big Data Lifecycle - Episode 94

Summary

Managing big data projects at scale is a perennial problem, with a wide variety of solutions that have evolved over the past 20 years. One of the early entrants that predates Hadoop and has since been open sourced is the HPCC (High Performance Computing Cluster) system. Designed as a fully integrated platform to meet the needs of enterprise grade analytics it provides a solution for the full lifecycle of data at massive scale. In this episode Flavio Villanustre, VP of infrastructure and products at HPCC Systems, shares the history of the platform, how it is architected for scale and speed, and the unique solutions that it provides for enterprise grade data analytics. He also discusses the motivations for open sourcing the platform, the detailed workflow that it enables, and how you can try it for your own projects. This was an interesting view of how a well engineered product can survive massive evolutionary shifts in the industry while remaining relevant and useful.

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  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Counsil. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
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  • Your host is Tobias Macey and today I’m interviewing Flavio Villanustre about the HPCC project and his work at LexisNexis Risk Solutions

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what the HPCC system is and the problems that you were facing at LexisNexis Risk Solutions which led to its creation?
    • What was the overall state of the data landscape at the time and what was the motivation for releasing it as open source?
  • Can you describe the high level architecture of the HPCC platform and some of the ways that the design has changed over the years that it has been maintained?
  • Given how long the project has been in use, can you talk about some of the ways that it has had to evolve to accomodate changing trends in usage and technologies for big data and advanced analytics?
  • For someone who is using HPCC, can you talk through a common workflow and the ways that the data traverses the various components?
    • How does HPCC manage persistence and scalability?
  • What are the integration points available for extending and enhancing the HPCC platform?
  • What is involved in deploying and managing a production installation of HPCC?
  • The ECL language is an intriguing element of the overall system. What are some of the features that it provides which simplify processing and management of data?
  • How does the Thor engine manage data transformation and manipulation?
    • What are some of the unique features of Thor and how does it compare to other approaches for ETL and data integration?
  • For extraction and analysis of data can you talk through the capabilities of the Roxie engine?
  • How are you using the HPCC platform in your work at LexisNexis?
  • Despite being older than the Hadoop platform it doesn’t seem that HPCC has seen the same level of growth and popularity. Can you share your perspective on the community for HPCC and how it compares to that of Hadoop over the past decade?
  • How is the HPCC project governed, and what is your approach to sustainability?
    • What are some of the additional capabilities that are only available in the enterprise distribution?
  • When is the HPCC platform the wrong choice, and what are some systems that you might use instead?
  • What have been some of the most interesting/unexpected/novel ways that you ahve seen HPCC used?
  • What are some of the challenges that you have faced and lessons that you have learned while building and maintaining the HPCC platform and community?
  • What do you have planned for the future of HPCC?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Click here to read the raw transcript...
0:00:14
Hello, and welcome to the data engineering podcast the show about modern data management. When you're ready to build your next pipeline or want to test out the projects you hear about on the show, you'll need somewhere to deploy it. So check out our friends over at the node. With 200 gigabit private networking, scalable shared block storage in the 40 gigabit public network. You've got everything you need to run a fast, reliable and bulletproof data platform. If you need global distribution they've got that coverage to with worldwide data centers, including new ones in Toronto and Mumbai. And for your machine learning workloads. They just announced dedicated CPU instances, go to data engineering podcast.com slash node that's LINODE today to get a $20 credit and launch a new server in under a minute. And don't forget to thank them for their continued support of this show. For even more opportunities to meet, listen and learn from your peers you don't want to miss out on this year's conference season. We have partnered with organizations such as O'Reilly Media Day diversity in the Open Data Science Conference with upcoming events including the O'Reilly AI conference, the strata data conference, and the combined events of the data architecture summit and graph forum. Go to data engineering podcast.com slash conferences to learn more and to take advantage of our partner discounts when you register. Your host is Tobias Macey, and today I'm interviewing Flavio Villeneuve, about the HPC project and his work and his work at Lexis Nexis risk solutions. So Flavio, can you start by introducing yourself,
0:01:36
of course to be so my name is fluid ministry, I'm Vice President of technology and ca. So for Lex and XV solutions. We in the electronics solutions, we have a data platform called the HB systems platform, we have made it both from open source in 2011. And since then, I've been also as part of my role involved with leading the open source community initiative. ensuring that the open source community truly leverages the platform helps contribute to the platform, and certainly creating a liaison between the next Nexus solutions organization and the rest of the larger open source community.
0:02:19
And do you remember how you first got involved in the area of data management
0:02:22
thoroughly has been seamless, and it's probably in the early 90s been going through the database to the database analytics to data management to Data Integration? Keep in mind that even within Lex Nexus, we started the HP systems platform back earlier before year 2000. So back then we already had data management challenges with traditional platforms. And we started with this. And I've been involved since I joined the company in 2002, just with HPC. But before I've been in data management for for a very long time.
0:02:57
And so for the HPC system itself, can you talk through some of the problems that it was designed to solve and some of the issues that you were facing at Lexis Nexis that led to its original creation?
0:03:10
Oh, absolutely. So in Mexico, we solutions, we started with management, I say, our core competency back in the mid 90s. And as we go into risk management, one of the core assets when you are trying to assess risk, and predict outcomes is data. Even before people spoke about big data, we had a significant amount of data, mostly structured, semi structured data to but the vast majority structured. And we used to use the traditional platforms out there, whatever we could get our hands on. And again, this is old, back in the day before Hadoop. And before MapReduce was applied as a distributed paradigm for data management or anything like that. So databases, Sybase, Oracle, whatever was Microsoft SQL, data management platforms of initio information, whatever was available at the time. And certainly the biggest problem we had with a scalable, but was twofold one was the scalability, all of those solutions typically run in a single system. So there is a limit to how much bigger you can go vertically. And certainly, if you're trying to also consider cost affordability of the system. And that limit is that is much lower as well, right, there is a point where you go beyond what the commodity system is, and you start paying a premium price for whatever it is. So that was the first piece. So one of the one of the attempts of solving this problem was to split the data and use different systems but splitting the data, it creates also challenges around data integration, if you're trying to link data, surely you can take the traditional approach, which is you segment your data into tables. And you put those tables in different databases, and then use terms of the foreign key to join the data. But that's all good and dandy as long as you have a foreign key that is unique, handheld is reliable. And that's not the case with data that you acquire from the outside. If you didn't read the data, you can have that if you bring the data from the outside, you might have a record that says these records about john smith, and you might have another record that says this liquid Mr. john smith, but do you know for sure he does. Two records are about the same john smith. And that's, that's a Lincoln problem. And the only way that you can do Lincoln effectively is to put all the data together. And now you have a we have this particular issue where in order to scale, we need to segment the data, in order to be able to do what we need to do, we need to put the data in the same data lake as a dome team. Later, we used to call this data land, eventually we teach it term in the late 2000s. Because data lake become became more more well known. So at that point, the potential bats to overcome the challenge where well, we either split all of the data as we were before, and then we come up with some sort of meta system that will leverage all of these 3d data stores. And potentially, when when you're doing prolific linkage, you have problems that are in have the computational complexity always square or worse. So that means that we will be a significant price and performance but potentially can be done if you have enough time and your systems are big enough, and you have enough bandwidth in between the systems. But the complexity you're gaining from a programming standpoint is also quite significant. And
0:06:33
some things you don't
0:06:34
have enough time some things you get data updates that are maybe hourly or daily. And the doing this big linking process may take you weeks or months if you're doing this across different systems. So and the complexity in programming, this is also pretty significant factor to consider. So at that point, we thought that maybe better approach to these was to create them. So defend an underlying platform to apply this type of solutions to problems with algorithms in a divide and conquer type of approach, we would have something that would partition the data automatically. And that will distribute the data in partitions into different commodity computers. And then we would add an abstraction layer on top of it that would create a programming interface that gave you the appearance that you are dealing with a single system with a single data store. And whatever you coded for that data store would be automatically distributed to the underlying partitions. We would also because of the way the hardware was fighting slower than it is today, we thought that a good idea would be to move also as much of the algorithm as we could to those partitions rather than executing the centrally. So instead of bringing all of the data to a single place to process these, which the single place might not have enough capacity would be to do as much as you can for a couple of brief field operation or a distributed grouping operation or through the filtering operation across each one of the politicians. And eventually, once you need to do the global aggregation, you can do it centrally. But now with a far smaller data set that is already pre filter. And the time came to define how to build abstraction layer. The one thing that we knew about was SQL as a programming language. And we said, well, this must be something that we can track with SQL as a permanent interface for our data analysts. But they work with us quite used to a data flow model because of the type of tools they were using before. Things like a couple of an issue where the data flows are these diagrams where your notes are the operation. So the activities you do and the data and the lines connecting the flow, the activities represent the data traversing those. So we thought that a better approach than SQL would be to create the language that a gave you the ability to build this sort of data flows in that system. That's how easy it was born, which is the language that runs on WHVZ and HPC.
0:09:05
So it's interesting that you had all of these very forward looking ideas in terms of how to approach data management, well, in advance of when the overall industry started to encounter the same types of problems as far as the size and scope of the data that they were dealing with that led to the rise of the Hadoop ecosystem, and the overall ideas around data, lakes and MapReduce and some of the new data management paradigms that have come up. And I'm wondering what the overall landscape looked like in the early days of building the HPC system that required you to implement this in house and some of the other systems or ideas that you drew on for inspiration for some of these approaches towards the data management and the overall systems architecture for HPC?
0:09:52
That is a great question. So it's interesting, because in the early days, when we told people what we were doing, they will look as bad often asked, Well, why don't you use database x, y, z, or data management system XYZ. And the reality is that none of those would be able to cope with the type of data, frequent data process, they wouldn't offer the flexibility of the process, like this probabilistic record linkage that we that I explained before that we do, and certainly good an offer in seamless transition between data management and data forwarding, which was also one of the important requirements that we had a time, it was quite difficult to explain to others why we were doing this, and what we were gaining by doing this. So map and reduce operations as, as functional programming operations have been around for a very long time since the list Lyft days in the 50s. But the idea of using map and with us as operations for the data management didn't get published and build this, I think was September December 2004. And I remember reading the original paper from the Google researchers thinking, well, now someone else has the same problem. And they got to do something about it. Even though at the time we were already we already have HPC. And we already had the CL so it was a perhaps too, too late to go back and try to re implement the data management aspects and the and the programming layer abstraction on HPC. So just for those people in the audience that don't know much about the CL, again is this is all open source or open source and free Apache to license and there are no no strings attached. So please go there and look at it. But in summary, ECL is a declarative Dataflow programming language. And not unlike declarative manner, what you can find in an SQL or functional programming languages, Haskell maybe wait of Bremen, Lisp and closure and other permanent oh there. But if data flow, from their standpoint is closer to something like TensorFlow, if you are familiar with TensorFlow as deep learning, programming paradigm, and framework, so where you could data operations that are primitive, that our data primitives, like for example, sort, you can say sort data set by this column in this order. And then you can add more modifier if you want, you can do a join across data sets. And again, the abrasions join, and you can do a roll up operation and operations name roll up. All of these are high level operations, you define them in your program. And in a declarative programming language, you create definitions, rather than assign variables. For those that are not familiar with declarative programming. And so many are in this audience. The collective programming has, for the most part, the property of having immutable data structures, which doesn't mean that you cannot do valuable work. And you can do all of the work the same way or better. But it gives get gets rid of side effects and other pretty bad issues with a more traditional immutable data structures. So you define out to you to define things, I have a data set that has a phone book, and I want to define an attribute that is this data set, filter by a particular variable. And then I might define another attribute that uses the filter data set to now group it in a particular way. So at the end of the day, any single program is just a set of definitions that are compiled by your compiler. And these compilers is yelling to see which then men reality c++, which then goes into the c++ compiler of the system, this is your plan or whatever you have and generate assembly code. And that that is the goal that is run in the platform. But the fact that you feel is such a high level programming language, and the fact that is declarative means that the CL compiler can take decisions that otherwise more imperative type of programming language wouldn't allow the compiler to take the compiler in a declarative programming language. And functional languages is also in case knows the ultimate goal of the program, because the problem is, in some ways, is a morphic to an equation. And you could even line from a functional standpoint, every one of your statements into a single massive statement, which you of course, can do from a clinical standpoint. But the compiler can now for example, do things like apply non strictness, if you put a statement, if you made a definition that is never going to be used, there is no point for that definition to be even compiled in or executed that all that saves performance equal. If you have a conditional fork in a place in your in your code, but that condition is always met or never met, then there is no need to compile the other branch a all of these gives you performance implications that can be far more significant. When you're dealing with big data. One of the particular optimizations can be around data and calculation, it is a lot far, it's far more efficient than a lot faster, if you are going to do similar operations to every legislator said to combine all of those operations, and do only one person to data with all the abrasions if it's possible. And they combine laser compatible as exactly that. And, and takes away a little bit of the perhaps flexibility on the programmer by making it far more intelligent at the moment, it's compiled. Of course parameters can tell the compiler I know better and forced to do something that may be otherwise unreasonable. But a just an example. You could say, well, I want to sort this data set and I then I want to filter it out and get only these few records. And if you say that in that order there, a an embedded the programming language would first sort and sort of even in the optimal, most optimal case is it's an N login type of operation and condition of complexity, and then fill it out and get only a few records out of it, when the optimal situation would be to filter it out first, and get those few records and then sort those records and ACL competitors. exactly that.
0:16:01
The fact that the language that you're using for defining these manipulations ends up being compiled. And I know that it's implemented and C and c++, both the ACL language itself as well as the overall HPC platform is definitely a great opportunity for better performance characteristics. And I know that in the comparisons that you have available for going between HPC and Hadoop, that's one of the things that gets called out. And as far as the overall workflow for somebody who is interacting with the system using that ECM language. I'm curious if the compilation step ends up being in any way a not a hindrance, but a delaying factor as far as being able to do some experimental iteration or if there is the capability of doing some level of interactive analysis or interaction with the data for being able to determine what is the appropriate set of statements to be able to get the desired end result when you're building an ACL data flow?
0:17:05
Nice. Another great question, I can see that quite diverse
0:17:10
and programming. So you're right, the fact that the seal is compiled means that just again, for for the rest of the audience, we have an integrated development environment policy, like the and of course, we support other like Eclipse and Visual Studio and all of the standard ones, but I'll just talk about it, feel it because it's what I mostly use. In that case, when you write code, you write the ATL code, and then you do, you can certainly run the test of the Golden but if you verify that that gold is, is correct, syntactically, but at some point, you want to run the gold because you want to get it in, you didn't want to know if semantically makes sense, and it will give you the right results. Right so and running the gold we go through the compilation process, depending on how large your code bases, certainly the competition process can take longer. Now the compiler does know what can be modified. Remember, again, a Felisa declarative programming language. So if you haven't touched a number of attributes, and again, data structures are immutable, and add to use the DOM change, since there are no side effects should be exactly the same. So the fact that a when you define a function, that function cause referential transparency, that means that if you call the function at any time, it will give you the correct result, or the same result based on the parameter and just based on the parameter that you're passing with that the compiler can take some shortcuts. And if you are re compiling some bunch of UCL attributes, but you haven't done too many of them, it will just use the pre compiled code for those and only compile those of you have changed. So the completion process, when you are dead, delicately working on code tends to be fairly quick, maybe a few seconds, of course, you depend on having any car company find it available. Traditionally, we used to have a centralized approach to the Excel compiler, when it would be one or a few of them running in the system, we have moved to a more distributed model where when you deploy your refill ID and you refill tools in your workstation, there's a compiler that goes there. So the field completion process can happen in the workstation as well. And that gives you the ability to have it available for you at all times when you're trying to use it. The one of the bottlenecks was at some point before, when you were trying to do this quick adaptive programming approach to things and the compiler was being used by someone that was compiling a massive amount of PCs from some a completely new job, and may have taken minutes and you were does they are sitting, picking your nose waiting for the compiler to to finish that one completed. By the way, the time to compile this is an extremely important consideration. And we continue, we improved the compiler to make it faster. We we have learned you can imagine over a bit. By the way, some of the same core developers have developed the CL compiler governing holiday, for example, have been with us since the very beginning they he was one of the core architects became the initial design of the platform. And he's still the lead architect that is developing that ECM compiler, which means that a lot of the knowledge that has gone into into the compiler process and optimizing it is still getting better and better. Of course, now with the larger community working on the compiler and and more people involved and more documentation around it means that others can pick up where he leaves. But hopefully he will be around and doing this for a long time. And making sure that the compiler is as Justin time as it can be is is very there is no at this point interpreters for ECL. And I think it would be quite difficult to make it completely interactive where the point where you submit just a line of code and does something because of the way a declarative programming paradigm works, right.
0:21:17
And also, because of the fact that you're working most likely, with large volumes of data distributed across multiple nodes, being able to do a rebel driven development is not really very practical, or it doesn't really make a lot of sense. But the fact that there is this fast compilation step in the ability to have a near real time interactivity, as far as seeing what the output of your programming is, it's good to see particularly in the Big Data landscape, where I know that the overall MapReduce paradigm was plagued in the early years by the fact that it was such a time consuming process to submit a job and then see what the output was before you could then take that feedback and wrap that into your next attack. And that's why there has been so many different layers added on top of the Hadoop platform in the form of pig and hive and various sequel interfaces to be able to get a more real time and interactive and iterative development cycle built in.
0:22:14
Yeah, you're absolutely right there. Now, one thing that I haven't told the audience yet is how the platform looks like mine. And I think that this we are getting to the point where it's quite important to explain that there are two main components in the HPC systems platform, there is one component that does data integration, these these massive data management engine equivalent to your data lake management system, which is called for for is meant to run one PCL work unit at a time which a What can it can consist of a large number of abrasions and many of them are running parallel Of course, and there is another one which is known as Roxy which is the data delivery engine there is one which is a sort of a hybrid called AH for now Roxy an H store both are designed to operate in 10s of thousands or more operations at the same time simultaneously, for is meant to do one work unit at a time. So, when you are developing on for even though your completion process might be quick, and you might run on a small data sets quickly, because you can execute this work in it on those little data sets using For example, h4, if you are trying to do the data in large data transformation of a large data sets in your phone system, you still need to go to the queue in that for and you will get your time whenever it's due for you right, surely you can we have priority, so you can jump into a higher priority queue and maybe you are you can be queued after a the just the current job. But before any other future jobs, we also partition jobs into smaller unit. And those smaller units can be also segmented, they are fairly independent from each other. So we could even interleaved some of your your jobs into in between a job that is running by getting into each one of those segments of the of the work unit. But if they get active in this there is a little bit less than a than optimal, but it is the nature of the basis because you want to have a large system to be able to process this throughout all the data in a relatively fast manner. And if we were trying to truly multi process they are most likely many of the resources available, good suffer, so you may end up paying a significant overhead across all of the president or running in parallel. Now. I did say that full run only one working at a time. But that was a little bit of a lie. That was really a few years ago. Today, it does run you you can define multiple QC in a store. And you can make run 34 then work units, but certainly not thousands of them. So that's a that's a big difference between that and Roxy, can you run your work in it and Roxy, yes, or in each floor. And they will run concurrently with anything else that is running with almost no limit their thousands and thousands of them can run at the same time. But there are other considerations on when you run things on Roxy or H store versus in floor. So it might not be what you really want.
0:25:29
Taking that a bit further, can you talk through a standard workflow for somebody who has some data problem that they're trying to solve and the overall lifecycle of the information as it starts from the source system gets loaded into the storage layer for the HPC platform. They define an ACL job for that then gets executed and Thor h store and then being able to query it out the other end from Roxy and just the overall systems that get interacted with each other rage about data life cycle.
0:26:01
co I love to so very well let's let's set up something very simple. As an example, you have a number of data sets that are coming from the outside, you need to load those data sets into HPC. So the first operation that happens is something that is known as spray spray is simple process is an spray comes from the concept of spray painting the data across the cluster, right. So this runs on a Windows box or a Linux box and it will take the data set, let's say that your data set is just given number in million records long. It will unusual as it can be in any format, CSV or or any other or fixed length limited or whatever. So it will look at your data total data set, it will look at the size of the four cluster where the data will be saved initially for processing. And let's say that you have a million records in your data set and you have MN nodes on your for let's just make round numbers and the small numbers. So it will a petition the dataset into 10 partitions because you have to note and it will a then just copy transfer each one of those partitions to the corresponding to full node This is done. If it can be better lies in some way, because for example, your latest fix link, it will automatically use pointers and paralyze this if the data is in either no and XML format or in the limited format where it's very hard to find the partition points, you will need to do a pass in the data, find the friction points and eventually do the panel copying to the thought system. So now you will end up with 10 partitions of the data with the data in no particular order, the Netherlands, all of them that you had before, right. So the first 100,000 records will go to the first note the second 100,000 Records, we go to the second node and so on so forth until you go to the end of the data set this put each one of the nodes in a similar amount of records per node, which tends to be a good thing for most processes. Once the data is spread or
0:28:10
while the data has been sprayed. And depending on the length of the data,
0:28:13
or or even before year, you will most likely need to arrive at work you need to work on the data. And I'm trying to do this example in a way that he said that data The first thing you see that data. So otherwise, all of these automated, right, so you don't need to do anything manually. All of this is scheduled and automated. And working that you already had will run on the new data set and will have appended or whatever it needs to be done. But let's imagine that is completely. So now you write your work unit. And let's say that your latest said was a phone book, and you want to first of all, and even a duplicate, build some rollout views on the phone book. And eventually you want to allow the users to run some queries on a web interface to look up people in the phone book. So you and let's just for the sake of an argument argument, let's say that you're also trying to join that phone book with your customer contact information. So, you will right they will connect that it will have that join to merge those two, you will have some duplication and perhaps some sort of thing. And after you have that you will need to build you will want you don't need to but you will want to build some keys. There is another again, key build processing for the oldest runs on for that will be part of your work unit. So essentially, it's all the CO writer working with ECL submit their work unit, they still will be compile will run on your data, hopefully, they feel will be syntactically correct when you submit it. And it will run with giving you the resource that you were expecting on the data. You see. I mentioned this before, but he says surgical type language as well, which means that it is a little bit harder to errors that will only appear in runtime between the fact that he has no side effects. And that is typically typed most typing errors, type errors they've made in errors and they might into function operations errors are a lot less frequent. There is not like Python, but you may
0:30:17
seem okay. The
0:30:20
run may be fine. But then one run at some point it will give you some we are there because a variable that was supposed to have a piece of text has a number to revise the verse. So you run the work in it, they will give you the result as a result of this work unit will give will potentially give you some statistics and the data some metrics. And he will give you a number of keys, those keys will be also partitioned in four. So there will be filtered nodes, the keys will be partitioning them pieces in those nodes. And you will be able to play those keys as well from for Joe, you can write a few attributes that can do the quoting there. But at some point you will run to you will want write those queries for Roxy to us. And you will want to put the date and Roxy because you don't have one user creating the data you will have a million users going to query that data and perhaps a 10,000 of them will be simple things liquidating. So for the process, you write a another piece of ECL another sort of work in it, but we call this query and you submit that to Roxy instead of four. And there is a slightly different way to submit it to Roxy. So you select Roxy and you submit this, the difference between this query and they work in it I do the heat you have in four is that the query is parameter raised and similar to paradise to proceed in your database, you will find some variables that are supposed to be coming from the front end from the input from the user. And then you just use the values and those variables to run some of the whatever filters or or aggregations that you need to do there, which will work in Roxy and will leverage the keys that you have from for i said before the keys are not mandatory, Roxy can perfectly work without keys can even cast a way to work with in memory distributed data sets as well. So even if you don't have a key, you don't pay a significant price in they look at by doing the sequential look up on the data and the full table scans of your database. So you submit that to Roxy, when you submit that query to Roxy, Roxy will realize that it has the data and it's not in Roxy's in for and this is also your choice, but most likely you will just tell Roxy to load the data from for it will know what to all the data from because he knows what are the keys are and what the names of those keys are, it will automatically load those keys. And also your choice to the Roxy to a stair allowing users to query the front an interface or to a while it's loading the data or to wait until the data is loaded before it allows the queries to happen. The moment you submit the query to Roxy, Roxy will automatically exposed on the front end there is a component called ESP, that component called DSP exposes a web services interface. And this gives you a restful interface, a soap interface, JSON for the payload, if you're going from the restful interface, even AM an old EBC interface if you want. So you can have unit even SQL and front on the front end. So the moment you submit the query, the query automatically generate out to generates these, all of these web service interfaces. So automatically, if you want to go with a web browser on the front end, or if you have an application that can use I don't know a restful interface over HTTP or HTTPS, you can use that and it will automatically have access to that Roxy quality that you submitted, of course, a single Roxy might have not one query but 1000 different queries at the same time, a all of them leasing an interface, and it can have several versions of the same of the queries as well. The queries are all exposed version from the front end. So you know, what they use is an accent. And if you are deploying a new version of equity or modified and extinguish it, you don't burn your users, if you don't want to, you give them the ability to migrate to the new version as as they want. And that's it. That's pretty much the process. Now, as you have committed to these while you need to have automation, all of these can be fully automated. In ECL, you may want to have data updates. And I told you data is immutable. So every time you think you're mutating data, you're updating data, you're really creating a new data set, which is good because it gives you full provenance, you can go back to your everyday version, of course, at some point, you need to delete data, or you will run out of space. And that can be also automated. And if you have updates on your
0:34:36
data, we have concepts like super files where you can apply updates, which are essentially new overlays from the existing data. And the existing work unit can just work on that, happily as if it was a single data set. So a lot of these complexities in the that otherwise will be exposed to the user to developer are all abstracted out by the system. So the developers if they don't want to see the underlying complexity, they don't need to, if they do they have the ability to do that I mentioned before Well, ECL will optimize things. So if you tell it, do this, join, but before doing the join to the sword, well, you may know that it is to us or to the sort of won't be that. But a if you know that your latest resorted, you might say, well, let's not do this, or I want to do this join each one of our politicians locally, instead of a global join, and order they are the same thing with sort of disorder operation and ECL of course, if you tell it to do that, and you know better than than the system, you see, I will follow your orders. If not, it will take the safe approach to your operation. Even if it's a little bit more overhead. Of course,
0:35:47
a couple of things that I'm curious about out of this are the storage layer of the HPC platform and some of the ways that you manage redundancy and durability of the data. And I also noticed when looking through documentation that there is some support for being able to take backups of the information, which I know is something that is non trivial when dealing with large volumes. And also on the Roxy side, I know that it maintains an index of the data. And I'm curious how that index is represented and maintained and kept up to date in the overall lifecycle of the platform.
0:36:24
Those are also very good question. So in the case of for for him Cassie concept. So we need to go down to a little bit of a system architecture. So in Thor you have each one of the nodes that handle a primarily they are chunk of data, they are partition of the data. But there is always a body node, some other node that has also their own partition, but they have a copy of the partition of some other nodes. If you have 10 nodes in your cluster view your node number one, I have the first partition and my have a copy the partition that no den has no number two might have a partition number two, but also might have a copy of the partition that no no number one has, and so on so forth. every node would have one primary partition and one backup partition of the other nodes every time you run a work unit. He said that you did he mutable, but you are generating a new data set every time that you are materializing data on the system, either by forcing it to materialize or a by letting the system materialize the data when it's necessary. And the system tries to stream as much in this way similar more similar to spark or or TensorFlow where the data can be streamed from acuity to acuity without being materialized. And like my previous and at some point, he decides that it's the time to materialize because the next operation might require materialized data or because you've been going for too long with data that if something goes wrong with the system will be blown up with every time it materializes data, the lazy copy happening with a new data has materialized to these backup nodes. So surely there is there could be a point where if something goes very wrong, and one of the nodes dies and the data in the disk is corrupted, but you know that you have always another know that has ad copy. And the moment you replace you do with known as Khufu essentially pull it out put another one in the system will automatically revealed that missing partition because it has complete redundancy of all of the data partitions in all the different nodes in the case of Roxy. So in the case of Florida seems to be sufficient, there is of course, the ability to do backups. And you can backup all of these partitions which are just files in the Linux file system. So you can even back them up using any Linux backup utility or you can use HPC to backup for you into any other system you can have cold storage, some of the problems is what happens is where your data center is compromised. And now someone modified or destroyed the data life system. So you may have you may want to have some sort of offline backup. And you can all handle this in the normal system backup configuration, or you can do it the HPC and make it offloaded as well. But for Roxy, the redundancy is a even more critical in the case of for when a node dies, it is sometimes less convenient to let the system work in a degraded way. Because the system is typically as fast as the slowest node. If all nodes are doing the same amount of work, a process it takes an hour will take an hour. But if you happen to have one know the die that now there is one know that he's doing twice the work because he has to do deal with two partitions of data its own and the backup of the other one, the time to the process may take two hours. So it is more convenient to just stop the process when something like that happens. The note and let the system rebuild that note quickly and continue doing the processing. And that might take an hour and 20 minutes or 10 minutes rather than the two hours that otherwise you would have taken. And besides if a system continues to run and your drive your storage system died in one knows because it's old and there is a chance that either the storage systems, when they get under the same stress will die the same way you want to replace that one quickly and have a copy. As soon as you can do not run the risk that you lose two of the of the partitions. And if you lose two partitions that are in different nodes that are not the backup of each other, that's fine. But if you lose the primary node, and the backup node for that one, there is a chance that you may end up losing the entire partition which is which is bad. Again, bad if you don't have a backup and Leland returning back of some things next time. So it's it's also inconvenient. Now and the Roxy case, you are you have a far larger pressure to have the process continue. Because your Roxy system is typically explosive all to online production customers that may pay you a lot of money for you to be highly available.
0:41:06
So Roxy allows you to have define the amount of realness that you want. based on the number of copies that you want, you could say, well, I haven't been a Roxy and as as need, which is the default, a one copy of the data or I need three copies of the data. So maybe they copy the partition, we know the one will be will have a copy in two, or three and four, and so on so forth. Of course, you need four times the space. But you have a far Hager resilience, if something goes very wrong, and Roxy will continue to work, even if a nose is down or you know, top down or, or as many notes as you want that down as long as you have the data is still fine. Because worst case scenario if even if it was a partition completely Roxy Mike, if you want to continue to run, but he won't be able to answer any queries that we're trying to leverage that particular partition that he's gone, which is sometimes not a good situation, when you you ask about the format of the keys and the formatting, they have the keys of the indexes in Roxy is interesting, because those keys, which is again, typically the format of the data that you have in Roxy, for the most part, you will have a primary key, these are all keys that are multi field like in a normal decent database out there. So they have multiple fields a they go, typically they those fields are all over by cardinality. So the fields with the larger cardinality will be at the front to make it more better performing. It has interesting abilities, like for example, you're going to step over a field that you don't have, you have a Wildcat for and still use the remaining fields, which is not something that normally a database doesn't do. Once you have a field that you don't have a value to apply, the rest of the fields on the right hand side are useless. And those Mexico's other things that are quite interesting there. But the way the data is stored in those keys is by decomposing those keys into two components, there is a top level component that indicates which node will have that partition. And there is a bottom level component, which indicates Where in the hell drive they have that a of the node, the specific data elements or the specific block of data elements are. So by decomposing the keys in these two hierarchical levels, it means that every node in Roxy can have the top level of that which is very small. So every node can know where to contact the specific values. So every note can be quoted from the front end, you have now a good scalability on the front end, you can have a load balancer and load balance all of the nodes. And it still on the back end, they can go back and know which node to ask for this when I said that the bottom level has the specific partition, I lied a little bit because he's not been no number one uses multicast. So nodes, when they have a partition of the data they subscribed a multicast channel, what you have in the top level is the number of the multicast channel that will handle that partition that allows us to make Roxy nodes more dynamic and handle. Also the fault tolerance situations where nodes go down. Well, it doesn't matter if you send the message to a multicast channel. Any know that is correct, we get the message, which one to respond well, he will be there faster note they know that is less burdened by other queries, for example. And if any know dies in the channel, it really doesn't matter. You're not stuck in a TCP connection waiting for the handshake to happen because they know the wind the way it is UDP, you send the message, and you will get the response. And of course, if nobody responded in a reasonable amount of time, you can resend that message as well,
0:44:53
going back to the architecture of the system, and the fact that how long it's been in double element and use and the massive changes that have occurred at the industry level as far as how we approach data management and the uses of data and the overall system that we might need to integrate with. I'm curious how the HPC platform itself has evolved accordingly. And some of the integration points that are available for being able to reach out to or consume from some of these other systems that organizations might be using,
0:45:26
we have changed quite a bit. So even those HPC systems name and some of the code base is resembles what we have 20 years ago, as you can imagine, any piece of software, he's a living living entity, and changes and evolved under that I've got as long as the communities active behind us, right. So we have changed significantly, we have not just added functionality, core functionality of HPC or change the functionality I had to adapt to times, but also build integration points. I mentioned a spark for example, and spark. Even though HPC is very similar to spark. spark is a large community around machine learning. So it is useful to integrate with the spark because many times people may be using spark ml. But they may want to use HPC for data management. And having a proper integration where you can run a spark ml and have on top of HPC is something that can be attracted to a significant amount of the HPC open source community. In other cases, like for example, Hadoop and HDFS axes are the same way integrations with other programming languages. Many times people don't feel comfortable programming everything in the CL and ECM works very well for a Data Manager something that is a data management centric process. But sometimes you have little components in the process, for example, that cannot be easily expressed in ECL is not in a way that is efficient.
0:46:55
And I don't know, I'll just throw one little unit together unique, unique ideas for things and you want to deny this you it is unique IDs in a random manner like UUIDs.
0:47:06
Surely you can call this and ECL, you can come back and come up with some crafty way of doing UCL. But he would make absolutely no sense to go to Denise EL, to then be compiled into some big chunk of c++, when I can go to directly in C or c++ or Python, or Java, or JavaScript. So being able to embed all of these languages into ECL became quite important. So we built quite a bit of integration for embedded languages is back even a few very major versions ago a few years ago, we added support for a I mentioned some of his language already Python, Java, JavaScript. And of course C and c++ was already available before. So people can add this little snippet songs functionality create attributes that are just embedded language type of attributes. And those are exposing CLS if they weren't UECO primitives. So now they have the ability of this and expand the ability of the core language to support new things without need to write them in a CL natively every time. And other there are plenty of other enhancements as well on the front end side. So I mentioned ESP ESP is this front end access layer, think of it as a some sort of message box in front of your Roxy system. In the past, we used to require that you code your ACL query for Roxy. And then you need to an ESP source recorded in c++. So you need to go to ESP and extend ESP with a dynamic model to support the front end interface for that query, which is twice the work. And you require someone that also knows c++ know just someone that knows ECL. So we change that. And we use something now that is called dynamic ESDL. That outdoor generates, as I mentioned before these interfaces from ESP, as you go this DCECL, all they want, they'll expect that you will put it there, you will call the query with some permitted eyes interface to a query. And then automatically GSB will take those parameters and expose those in this front end interface for for users to consume the decade, we also have done quite a bit of integration in systems that do that can help with the benchmarking of HPC. availability, monitoring, and performance monitoring all of the capacity planning of HPC as well. So we are we try to integrate as much as we can with our components in the open source community. We truly love open source projects. So if there is a project that already has done something that we can leverage, we try to stay away from reinventing the wheel every time we use it. If it's not open source, if it's commercial, we do have a number of integration with commercial systems as well. We are not to relate, we are not religious about it. But certainly it's a little bit less enticing to put the effort into something that is closed source. And again, we we believe that the model in open source, he says it's a very good model, because it gives us It gives you the ability to know how things have done under the hood and extended and fixed them if you need to. We do this all the time with our projects. We we believe that it has a significant amount of value for for anyone out there.
0:50:26
On the subject of the open source nature of the project, I know that it was released is open source. And I think you said the 2011 timeframe, which posts dates when Hadoop had become popular and started to accrue its own ecosystem. I'm curious what your thoughts are on the relative strength of the communities for Hadoop and HPC. Currently, given that there seems to be a bit of a decline in Hadoop itself as far as the amount of utility that organizations are getting from it, but also interesting in the governance strategy that you have for the HPC platform and some of the ways that you approach sustainability of the project.
0:51:08
So you're absolutely right, the community has apparently at least reached a plateau at psychological and HPC systems community, in number of people. Of course, it was the first to the open. So we have HVC for a very long time he was closed source, he was proprietary, and we didn't we at the time, we believed that he was so core to our competitive advantage that we couldn't afford to release it in any other way. When we realized that reality, the core advantage that we have is on one side data assets on the other side is the high level algorithms. We knew that the platform would be better sustained in the long Randy and sustainability is an important factor for the platform for us because the platform is so core to everything we do that we believe that making it open source and free, completely free as both a no just a freedom of speech, but also free beer. We we thought that that would be the way to ensure this long term sustainability and development and an expansion and innovation in the platform itself. But when we did that it was 2011. So it was a few years after Hadoop, Hadoop, if you remember, it started as part of another project around the web crawling and what called management, which eventually ended up It's a song top level Apache project in 2008, I believe. So it was already three or four and a half years after hundred was out there. And they're coming to us really large. So over time, we did gather a fairly active community. And today we have inactive a very technical, deeply technical community. That is that not just a helps with extending and expanding HPC, but also provides a VS use cases, sometimes interesting use cases of HPC and a and uses HPC in general and regular regularly. So he would it be system community continues to grow, the community seems to have reached a plateau. Now there are other communities out there, which also handle some of the data management aspects with their own platforms like spark I mentioned before, which seems to have a better performance profile than what Hello Cass. So it has been also gathered in active, active people in those communities. Well, I think open source is not a zero sum game where if a community grows, the other one will decrease and then eventually, the total number of people in the community will be the same across all of them. I think every new platform that introduces capabilities to open source communities and uses new ideas and and helps develops, apply innovation into those ideas is helping the overall community in general. So it's great to see communities like a spark community growing. And I think there's an opportunity, and many of the users in both communities are using both at some point for all of them to leverage what is that in the others. Surely, sometimes, the specific language using gold in the platforms, makes a little bit of a bit created a little bit of a barrier. Some of these communities are now just because of the way Java is potentially more common, that use Java instead of c++ and C. So you see that sometimes the people that are in one community who may be more versed in Java, feel uncomfortable going and trying to understand the code in the other platform that is coded in a different language.
0:54:52
But even even then, at least
0:54:55
semi generally VSVLO difference on the on the functions I capabilities can be extracted and used to be added. And I think this is good for the overall benefit of everyone. I see, in many cases open source as a as a experimentation playground, where people can go there can bring new ideas, apply those ideas to some code, and then everyone else eventually leverages them because these ideas percolate across different projects. It's It's It's quite interesting. Having been involved personally in open sources, the early 90s. I I'm quite fond of the of the process, open source work. I think it's it's beneficial to everyone in the in every community.
0:55:37
And in terms of the way that you're taking advantage of the HPC platform, Lexis Nexis and some of the ways that you have seen it used elsewhere. I'm wondering what are some of the notable capabilities that you're leveraging and some of the interesting ways that you've seen other people take advantage of it?
0:55:54
that's a that's a good question. And
0:55:56
that my the answer might take a little bit longer. So in the in Lexis Nexis, in particular, certainly we use HPC. For almost everything we do, because almost everything we do is data management in some way or data quality. Now, we have interesting approaches to things is we have a number of processes that are done on a on data. One of those is this prolific linkage process. And prolific linkage requires sometimes quite a bit of code to make it work correctly. So there was a point where we were ability to finish EL and he was creating a code base that was getting more and more sizable, larger, bigger, less manageable. So at
0:56:39
some point, we decided
0:56:41
that level of abstraction that is pretty high anyway, in ECL, wasn't enough for prolific data linkage. So we created another language we called it sold and we the unrelated language is open source, by the way, it's still providing, but that language is a language that is you're going to consider it a domain specific language for data Liggett productively only get and data integration, so that a compiler for salt, compile salt into CL, and they feel compelled by this EL into c++, c++, clang or GCC compiler into assembler. So you can see how abstraction layers or like layers in an audience, of course, every time you apply an improvement and optimization in the sale compiler, or sometimes the GCC compiler team applies an optimization. And you see everyone else on top of that, of that layer benefits from the optimization, which is quite interesting. We like it so much that eventually we have another problem, which is dealing with graphs. And when I say graphs, I mean social graphs rather than
0:57:46
charts.
0:57:47
So we built yet another language that deals with graphs and machine learning, and particularly machine learning in graphs, which is called Cal or knowledge engineering language, which by the way, we don't have an open source version, but we do have version of the compiler out there for people that want to try. So Gil, also generation CL, and E LD, my c++ and again, back to the same point. So this is a is an interesting approach to building abstraction by Creek, DSL, domain specific languages on top of ACL and other interesting application of HPC, outside of Nexus Nexus is there is a company that is it's called guard pad, they do have that are smart, they can do geo fencing for workers, they can do a detection of of risky environments, in manufacturing environment or in construction. And so they use HPC. And they use some of their real time integration that we have a with things like Afghan couch to be and other integrations I mentioned that we have word activity on integrating HPC with other open source projects to essentially manage all of these data, which is fairly real time.
0:58:57
And a create
0:58:58
this real time Allah and then real time, machine learning, execution for models that they have and integration of data and even visualization on top of it. And and there are more and more a good I could go for days, giving you some some of the ideas there of things that we have done an hour and or others in the community have done using HPC.
0:59:21
And in terms of the overall experience that you have had working with HPC on both the platform and as a user of it, what have you found to be some of the most challenging aspects and some of the most useful and interesting lesson for you've learned in the process?
0:59:38
That is a great question. And
0:59:40
I'll give you a very simple answer. And then I'll explain what I mean. What are some of the biggest challenges, if you are a new user is ECL. Some of the biggest benefits are ACL. Unfortunately, no, not everyone is, is well versed in declarative programming models. So when you are exposed for the first time to a declarative language that
1:00:04
has immutability and laziness. And
1:00:09
the no side effects, it makes sometimes a little bit of a brain twister in some way, right there, you get to, you need to think the problems in a slightly different way to be able to solve them. When you install that it used to embed the programming, you typically solve the problem by decomposing the problem into a just a recipe of things that the computer this process needs to do, step by step one by one, when you do the collective programming, you decompose the problem in a set of functions that need to be applied, and you build it from the ground up. This is slightly different type of, of approach. But it once you get the idea how this works, it becomes quite powerful for a number of reasons, it becomes quite powerful, because first of all, you get to understand the problem more, and you can express the algorithms in a far more succinct way, it would have been just a collection of attributes. And some of the attributes depend on other attributes that you have defined, it also helps you with better encapsulate the components in the problem. So now you're cold instead of becoming just some sort of a spaghetti that is hard to troubleshoot is willing calculated, both in terms of function and calculation, also dating calculation. So if you need to touch anything later on, you can do it safely without need to worry about what this function could be doing that I'm calling here to any to go and also look at the function because you know, there are no side effects. And it also gives you the ability to ECA if you of course, as long as you name your attributes correctly. So people understand what they they are attempting to do, are they they are supposed to do, you can collaborate more easily with other people as well. So after a while, I realized that I was building code in ECLM, and others have also the same way, then realize that they coded the writing the CL is, first of all, mostly correct most of the time, which is not what you do when you have a non declarative code programming. And you know that if the code compiles, there is high chance that the code will run correctly. And it will give you a correct results after it runs. And like I say, was explained before when you have a dynamically typed language is imperative programming with side effects were, surely they called my compile, and maybe it will run fine if your times, but one day, it may give you some sort of runtime error, because some type is mismatch or some side effect that you consider when you re architect some piece of the code now is kicking in and getting your your results different from what you expected. I think, again, a CL has been really quite quite a blessing from that standpoint. But of course, it does require that you learn this you want to learn and you learn this new methodology of programming, which could be similar to what someone that knows, Python or Java needs to learn in order to apply SQL and execute against another declarative language. So use on code SQL interactively. When you are trying to
1:03:34
query a database looking forward in terms of the medium to long term, as well as some of the near term for the HPC platform itself. What do you have planned for the future, both in terms of technical capabilities and features, but also as far as community growth and outreach that you'd like to see.
1:03:53
So from the technical capabilities and features, we tend to have a community roadmap of things and try to as much as we can to stick with those roadmap. So we have some, these big ideas that tend to get into the next or the following major version, these smaller ideas that are typical, non disruptive, and don't break past compatibility that go into the minor versions. And then of course, these bug fixes.
1:04:23
Like many say they are not bugs, but opportunities.
1:04:26
But in the great
1:04:28
at the big ideas side of things, some of the things that we've been doing is doing better integration of I mentioned before integration with other open source projects is quite important. We've been also trying to change some of the underlying components in the blood, there were some components that we have had for a very, very long
1:04:46
time, like, for example, the
1:04:48
underlying communication layer, in Roxy. And for that we think they may be right now for a further revamping, by incorporating some of the standard communications out there. There is also the idea of making the platform far more cloud friendly, even though it does run very well in many public clouds and OpenStack, and Amazon and Google and Azure. But we want to also make the clusters more dynamic. I don't know if you spotted when I said that when you when I explain how you do data management, we're too busy. And he said, Well, you have a note for Well, what happens when you want to change the tenor thought and make it a 20 or 30, or a five notes, or maybe you have a small process, that would work fine with just a couple notes or one knows, you have a large process that may need 1000 nodes. Today, you can dynamically resize the four cluster, surely you can do every if you can resize it by hand, and then do a reboot of the data and now have the data in the number of nodes that you have. But it is a lot more involved than we would like to see it with dynamic cloud environments, the facilities becomes quite important because that's one of the benefits of cloud. So making the classes also more elastic. more dynamic is another big goal. Certainly, we continue to develop machine learning capabilities. On top of it, we have a library of machine learning functions of their algorithms methods. And we are expanding that we sometimes have even some of these machine learning methods, which are quite, I would say innovative one of our core developers and also researchers developed a new distributed algorithm for K means clustering, which she hasn't seen in the literature before. So it's part of one one a paper and her PhD dissertation, which is very good. And the other one is also part of HPC. Now, so now people can leverage this, which gives a significantly higher scalability to K means, particularly if you're running a very large number of nodes, I'm going to get into the details and how it is it creates he said this far better performance. But in in some it distributes the data less. And instead the students the center, it's more and it uses the associative property of the gaming, the main loop of played k means clustering to try to minimize the number of data records that need to be moved around. That's it from the standpoint of the roadmap and the platform itself. On the community side, we continue to try to expand the community as much as we can. One of our core interests is to get I mentioned this core developer who is a also researcher, we want to get more researchers and an academia on the platform, we have a number of initiatives, collaboration initiatives, with a number of universities in the US and abroad university like Oxford University in the UK, University College London, Humboldt University in in Germany, and a number of universities in the US, Clemson University, Georgia Tech and Georgia State University and Annika so we want to expand this program more, we also have an internship program, we believe that one of the one of the things that we see are the goals that we want to achieve as well with with the HPC systems project open source project is to also help balance better the community behind it from balancing diversity across the community. So attracting both both generally but in general, generally vertically and regionally about diversity and background diversity. So we are trying to also put quite a bit of emphasis in students, even high school students, so we are doing quite a bit of activity with high schools, on one side trying to get them more into technology. And of course, learn HPC, but also the outside try to also get more women into technology get more people that otherwise wouldn't get into technology, because they don't get exposed to technology in their homes. And so that's another core piece of activity in HPC, the HPC community. Last but not least, as part of this diversity, there are certain communities that are a little bit more disadvantaged than others. One of those is people in the autism spectrum. So we have been doing quite a bit of activity with organizations that are helping these, these people. So also trying to enable them with a number of activities. And some of those have to do with training them into HPC systems as a platform and data management to give them open opportunities for them for their lives. Many of these individuals are extremely intelligent, they're they're brilliant, they may have other limitations because of their, their conditions. But they will be very, very valuable resources, not just flexible solutions. Ideally, we could tell you there but even for other organizations as well,
1:09:48
it's great to hear that you have all these outreach opportunities as well for trying to help bring more people into technology as a means of giving back as well as as a means of helping to be your community and contribute to the overall use cases that it empowers. So for anybody who wants to follow along with you or get in touch, I'll have you add your preferred contact information to the show notes. And as a final question, I'd like to get your perspective on what you see as being the biggest gap and the tooling or technology that's available for data management today,
1:10:19
I think there are a number of gaps, but the major one is, many of the platforms that are out there tend to be quite clunky, when it comes to integrating things. Unfortunately, we are at the point where we are not, I don't think we are mature enough. So I'm mature enough. I mean, if if you are a data management person, you know data very well, you know, data analytics, you know, data process, but you don't necessarily know operating systems, you don't know, you are not a computer scientist that can deal with data partitioning and computational complexity of algorithms in partition data. And, and there are many details that are necessary for you to do your job should be unnecessary for you to lose your job correctly. But unfortunately, today because of the state of things, many times many of these systems commercial and non commercial force you to take care of all of the details or assemble a large team of people, from system administrators to network, network administrators to operating system specialist to a middle layer, especially some build, you can build a system that can you do your data management, the and that's something that we we do try to overcome with HPC giving the screen in this homogeneous system that you deploy with a single command and that you can use a minute later, after you deployed it, I will say that we are in the ideal situation yet I think there is still much to improve on but I think we are a little bit further along than many of the other options out there. You if you know the the Hadoop ecosystem, you know, how many different components of that are out there. And you know, if you have done this for for a while, you know that one day you realize that they said either know a security vulnerability in one component MB, and now you need to update that. But in order to do that, you're going to break the compatibility of the new version with something else. And now you need to update that other thing. But there is no update for another thing, because that thing depends on another component. So yeah, and this goes on and on and on. So having something that is homogeneous, that it doesn't require for you to be computer scientist to deploy and use. And that truly enables you are the abstraction layer that you need, which is data management is a is a significant limitation of many, many systems out there. And again, not just pointing this at the open source projects, and also commercial product as well. I think it's something that some of the people that are designing and developing the systems might not understand because they are not the users. But they should think as a user, you need to put yourself in the shoes of the user in order to be able to do the right thing. Otherwise, whatever you build is pretty difficult to apply. Sometimes it's useless.
1:13:03
Well thank you very much for taking the time today to join me and describe the ways that HPC is built and architected as well as some of the ways that it's being used both inside and outside of Lexis Nexis. So I appreciate all of your time and all the information there. And it's definitely a very interesting system and one that looks to provide a lot of value and capability. So I appreciate all of your efforts on that front. And I hope you enjoy the rest of your day.
1:13:30
Thank you very much. I really enjoyed this and I look forward to doing this again. So one day we'll get together again. Thank you

Straining Your Data Lake Through A Data Mesh - Episode 90

Summary

The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access. In this episode Zhamak Dehghani shares an alternative approach in the form of a data mesh. Rather than connecting all of your data flows to one destination, empower your individual business units to create data products that can be consumed by other teams. This was an interesting exploration of a different way to think about the relationship between how your data is produced, how it is used, and how to build a technical platform that supports the organizational needs of your business.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to grow your professional network and find opportunities with the startups that are changing the world then Angel List is the place to go. Go to dataengineeringpodcast.com/angel to sign up today.
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Upcoming events include the O’Reilly AI Conference, the Strata Data Conference, and the combined events of the Data Architecture Summit and Graphorum. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Zhamak Dehghani about building a distributed data mesh for a domain oriented approach to data management

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by providing your definition of a "data lake" and discussing some of the problems and challenges that they pose?
    • What are some of the organizational and industry trends that tend to lead to this solution?
  • You have written a detailed post outlining the concept of a "data mesh" as an alternative to data lakes. Can you give a summary of what you mean by that phrase?
    • In a domain oriented data model, what are some useful methods for determining appropriate boundaries for the various data products?
  • What are some of the challenges that arise in this data mesh approach and how do they compare to those of a data lake?
  • One of the primary complications of any data platform, whether distributed or monolithic, is that of discoverability. How do you approach that in a data mesh scenario?
    • A corollary to the issue of discovery is that of access and governance. What are some strategies to making that scalable and maintainable across different data products within an organization?
      • Who is responsible for implementing and enforcing compliance regimes?
  • One of the intended benefits of data lakes is the idea that data integration becomes easier by having everything in one place. What has been your experience in that regard?
    • How do you approach the challenge of data integration in a domain oriented approach, particularly as it applies to aspects such as data freshness, semantic consistency, and schema evolution?
      • Has latency of data retrieval proven to be an issue in your work?
  • When it comes to the actual implementation of a data mesh, can you describe the technical and organizational approach that you recommend?
    • How do team structures and dynamics shift in this scenario?
    • What are the necessary skills for each team?
  • Who is responsible for the overall lifecycle of the data in each domain, including modeling considerations and application design for how the source data is generated and captured?
  • Is there a general scale of organization or problem domain where this approach would generate too much overhead and maintenance burden?
  • For an organization that has an existing monolothic architecture, how do you suggest they approach decomposing their data into separately managed domains?
  • Are there any other architectural considerations that data professionals should be considering that aren’t yet widespread?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Data Labeling That You Can Feel Good About - Episode 89

Summary

Successful machine learning and artificial intelligence projects require large volumes of data that is properly labelled. The challenge is that most data is not clean and well annotated, requiring a scalable data labeling process. Ideally this process can be done using the tools and systems that already power your analytics, rather than sending data into a black box. In this episode Mark Sears, CEO of CloudFactory, explains how he and his team built a platform that provides valuable service to businesses and meaningful work to developing nations. He shares the lessons learned in the early years of growing the business, the strategies that have allowed them to scale and train their workforce, and the benefits of working within their customer’s existing platforms. He also shares some valuable insights into the current state of the art for machine learning in the real world.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show!
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Mark Sears about Cloud Factory, masters of the art and science of labeling data for Machine Learning and more

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what CloudFactory is and the story behind it?
  • What are some of the common requirements for feature extraction and data labelling that your customers contact you for?
  • What integration points do you provide to your customers and what is your strategy for ensuring broad compatibility with their existing tools and workflows?
  • Can you describe the workflow for a sample request from a customer, how that fans out to your cloud workers, and the interface or platform that they are working with to deliver the labelled data?
    • What protocols do you have in place to ensure data quality and identify potential sources of bias?
  • What role do humans play in the lifecycle for AI and ML projects?
  • I understand that you provide skills development and community building for your cloud workers. Can you talk through your relationship with those employees and how that relates to your business goals?
    • How do you manage and plan for elasticity in customer needs given the workforce requirements that you are dealing with?
  • Can you share some stories of cloud workers who have benefited from their experience working with your company?
  • What are some of the assumptions that you made early in the founding of your business which have been challenged or updated in the process of building and scaling CloudFactory?
  • What have been some of the most interesting/unexpected ways that you have seen customers using your platform?
  • What lessons have you learned in the process of building and growing CloudFactory that were most interesting/unexpected/useful?
  • What are your thoughts on the future of work as AI and other digital technologies continue to disrupt existing industries and jobs?
    • How does that tie into your plans for CloudFactory in the medium to long term?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Scale Your Analytics On The Clickhouse Data Warehouse - Episode 88

Summary

The market for data warehouse platforms is large and varied, with options for every use case. ClickHouse is an open source, column-oriented database engine built for interactive analytics with linear scalability. In this episode Robert Hodges and Alexander Zaitsev explain how it is architected to provide these features, the various unique capabilities that it provides, and how to run it in production. It was interesting to learn about some of the custom data types and performance optimizations that are included.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show!
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Robert Hodges and Alexander Zaitsev about Clickhouse, an open source, column-oriented database for fast and scalable OLAP queries

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Clickhouse is and how you each got involved with it?
    • What are the primary use cases that Clickhouse is targeting?
    • Where does it fit in the database market and how does it compare to other column stores, both open source and commercial?
  • Can you describe how Clickhouse is architected?
  • Can you talk through the lifecycle of a given record or set of records from when they first get inserted into Clickhouse, through the engine and storage layer, and then the lookup process at query time?
    • I noticed that Clickhouse has a feature for implementing data safeguards (deletion protection, etc.). Can you talk through how that factors into different use cases for Clickhouse?
  • Aside from directly inserting a record via the client APIs can you talk through the options for loading data into Clickhouse?
    • For the MySQL/Postgres replication functionality how do you maintain schema evolution from the source DB to Clickhouse?
  • What are some of the advanced capabilities, such as SQL extensions, supported data types, etc. that are unique to Clickhouse?
  • For someone getting started with Clickhouse can you describe how they should be thinking about data modeling?
  • Recent entrants to the data warehouse market are encouraging users to insert raw, unprocessed records and then do their transformations with the database engine, as opposed to using a data lake as the staging ground for transformations prior to loading into the warehouse. Where does Clickhouse fall along that spectrum?
  • How is scaling in Clickhouse implemented and what are the edge cases that users should be aware of?
    • How is data replication and consistency managed?
  • What is involved in deploying and maintaining an installation of Clickhouse?
    • I noticed that Altinity is providing a Kubernetes operator for Clickhouse. What are the opportunities and tradeoffs presented by that platform for Clickhouse?
  • What are some of the most interesting/unexpected/innovative ways that you have seen Clickhouse used?
  • What are some of the most challenging aspects of working on Clickhouse itself, and or implementing systems on top of it?
  • What are the shortcomings of Clickhouse and how do you address them at Altinity?
  • When is Clickhouse the wrong choice?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Maintaining Your Data Lake At Scale With Spark - Episode 85

Summary

Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics. Delta Lake is an open source, opinionated framework built on top of Spark for interacting with and maintaining data lake platforms that incorporates the lessons learned at DataBricks from countless customer use cases. In this episode Michael Armbrust, the lead architect of Delta Lake, explains how the project is designed, how you can use it for building a maintainable data lake, and some useful patterns for progressively refining the data in your lake. This conversation was useful for getting a better idea of the challenges that exist in large scale data analytics, and the current state of the tradeoffs between data lakes and data warehouses in the cloud.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to keep track of how your team is progressing on building new pipelines and tuning their workflows, you need a project management system designed by engineers, for engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Data Engineering Podcast listeners get 2 months free on any plan by going to dataengineeringpodcast.com/clubhouse today and signing up for a free trial. Support the show and get your data projects in order!
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Michael Armbrust about Delta Lake, an open source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Delta Lake is and the motivation for creating it?
  • What are some of the common antipatterns in data lake implementations and how does Delta Lake address them?
    • What are the benefits of a data lake over a data warehouse?
      • How has that equation changed in recent years with the availability of modern cloud data warehouses?
  • How is Delta lake implemented and how has the design evolved since you first began working on it?
    • What assumptions did you have going into the project and how have they been challenged as it has gained users?
  • One of the compelling features is the option for enforcing data quality constraints. Can you talk through how those are defined and tested?
    • In your experience, how do you manage schema evolution when working with large volumes of data? (e.g. rewriting all of the old files, or just eliding the missing columns/populating default values, etc.)
  • Can you talk through how Delta Lake manages transactionality and data ownership? (e.g. what if you have other services interacting with the data store)
    • Are there limits in terms of the volume of data that can be managed within a single transaction?
  • How does unifying the interface for Spark to interact with batch and streaming data sets simplify the workflow for an end user?
    • The Lambda architecture was popular in the early days of Hadoop but seems to have fallen out of favor. How does this unified interface resolve the shortcomings and complexities of that approach?
  • What have been the most difficult/complex/challenging aspects of building Delta Lake?
  • How is the data versioning in Delta Lake implemented?
    • By keeping a copy of all iterations of a data set there is the opportunity for a great deal of additional cost. What are some options for mitigating that impact, either in Delta Lake itself or as a separate mechanism or process?
  • What are the reasons for standardizing on Parquet as the storage format?
    • What are some of the cases where that has led to greater complications?
  • In addition to the transactionality and data validation that Delta Lake provides, can you also explain how indexing is implemented and highlight the challenges of keeping them up to date?
  • When is Delta Lake the wrong choice?
    • What problems did you consciously decide not to address?
  • What is in store for the future of Delta Lake?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Unpacking Fauna: A Global Scale Cloud Native Database - Episode 78

Summary

One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale with your business.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
  • Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit.
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Evan Weaver about FaunaDB, a modern operational data platform built for your cloud

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what FaunaDB is and how it got started?
  • What are some of the main use cases that FaunaDB is targeting?
    • How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?
  • Can you describe the architecture of FaunaDB and how it has evolved?
  • The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?
    • What are some of the edge cases that users should be aware of?
    • How are conflicts managed in Fauna?
  • What is the underlying storage layer?
    • How is the query layer designed to allow for different query patterns and model representations?
  • How does data modeling in Fauna compare to that of relational or document databases?
    • Can you describe the query format?
    • What are some of the common difficulties or points of confusion around interacting with data in Fauna?
  • What are some application design patterns that are enabled by using Fauna as the storage layer?
  • Given the ability to replicate globally, how do you mitigate latency when interacting with the database?
  • What are some of the most interesting or unexpected ways that you have seen Fauna used?
  • When is it the wrong choice?
  • What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company?
  • What do you have in store for the future of Fauna?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Index Your Big Data With Pilosa For Faster Analytics - Episode 77

Summary

Database indexes are critical to ensure fast lookups of your data, but they are inherently tied to the database engine. Pilosa is rewriting that equation by providing a flexible, scalable, performant engine for building an index of your data to enable high-speed aggregate analysis. In this episode Seebs explains how Pilosa fits in the broader data landscape, how it is architected, and how you can start using it for your own analysis. This was an interesting exploration of a different way to look at what a database can be.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
  • Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit.
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Seebs about Pilosa, an open source, distributed bitmap index

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what Pilosa is and how the project got started?
  • Where does Pilosa fit into the overall data ecosystem and how does it integrate into an existing stack?
  • What types of use cases is Pilosa uniquely well suited for?
  • The Pilosa data model is fairly unique. Can you talk through how it is represented and implemented?
  • What are some approaches to modeling data that might be coming from a relational database or some structured flat files?
    • How do you handle highly dimensional data?
  • What are some of the decisions that need to be made early in the modeling process which could have ramifications later on in the lifecycle of the project?
  • What are the scaling factors of Pilosa?
  • What are some of the most interesting/challenging/unexpected lessons that you have learned in the process of building Pilosa?
  • What is in store for the future of Pilosa?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Building An Enterprise Data Fabric At CluedIn - Episode 74

Summary

Data integration is one of the most challenging aspects of any data platform, especially as the variety of data sources and formats grow. Enterprise organizations feel this acutely due to the silos that occur naturally across business units. The CluedIn team experienced this issue first-hand in their previous roles, leading them to build a business aimed at building a managed data fabric for the enterprise. In this episode Tim Ward, CEO of CluedIn, joins me to explain how their platform is architected, how they manage the task of integrating with third-party platforms, automating entity extraction and master data management, and the work of providing multiple views of the same data for different use cases. I highly recommend listening closely to his explanation of how they manage consistency of the data that they process across different storage backends.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems.
  • Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Tim Ward about CluedIn, an integration platform for implementing your companies data fabric

Interview

  • Introduction

  • How did you get involved in the area of data management?

  • Before we get started, can you share your definition of what a data fabric is?

  • Can you explain what CluedIn is and share the story of how it started?

    • Can you describe your ideal customer?
    • What are some of the primary ways that organizations are using CluedIn?
  • Can you give an overview of the system architecture that you have built and how it has evolved since you first began building it?

  • For a new customer of CluedIn, what is involved in the onboarding process?

  • What are some of the most challenging aspects of data integration?

    • What is your approach to managing the process of cleaning the data that you are ingesting?
      • How much domain knowledge from a business or industry perspective do you incorporate during onboarding and ongoing execution?
    • How do you preserve and expose data lineage/provenance to your customers?
  • How do you manage changes or breakage in the interfaces that you use for source or destination systems?

  • What are some of the signals that you monitor to ensure the continued healthy operation of your platform?

  • What are some of the most notable customer success stories that you have experienced?

    • Are there any notable failures that you have experienced, and if so, what were the lessons learned?
  • What are some cases where CluedIn is not the right choice?

  • What do you have planned for the future of CluedIn?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

A DataOps vs DevOps Cookoff In The Data Kitchen - Episode 73

Summary

Delivering a data analytics project on time and with accurate information is critical to the success of any business. DataOps is a set of practices to increase the probability of success by creating value early and often, and using feedback loops to keep your project on course. In this episode Chris Bergh, head chef of Data Kitchen, explains how DataOps differs from DevOps, how the industry has begun adopting DataOps, and how to adopt an agile approach to building your data platform.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems.
  • "There aren’t enough data conferences out there that focus on the community, so that’s why these folks built a better one": Data Council is the premier community powered data platforms & engineering event for software engineers, data engineers, machine learning experts, deep learning researchers & artificial intelligence buffs who want to discover tools & insights to build new products. This year they will host over 50 speakers and 500 attendees (yeah that’s one of the best "Attendee:Speaker" ratios out there) in San Francisco on April 17-18th and are offering a $200 discount to listeners of the Data Engineering Podcast. Use code: DEP-200 at checkout
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Chris Bergh about the current state of DataOps and why it’s more than just DevOps for data

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • We talked last year about what DataOps is, but can you give a quick overview of how the industry has changed or updated the definition since then?
    • It is easy to draw parallels between DataOps and DevOps, can you provide some clarity as to how they are different?
  • How has the conversation around DataOps influenced the design decisions of platforms and system components that are targeting the "big data" and data analytics ecosystem?
  • One of the commonalities is the desire to use collaboration as a means of reducing silos in a business. In the data management space, those silos are often in the form of distinct storage systems, whether application databases, corporate file shares, CRM systems, etc. What are some techniques that are rooted in the principles of DataOps that can help unify those data systems?
  • Another shared principle is in the desire to create feedback cycles. How do those feedback loops manifest in the lifecycle of an analytics project?
  • Testing is critical to ensure the continued health and success of a data project. What are some of the current utilities that are available to data engineers for building and executing tests to cover the data lifecycle, from collection through to analysis and delivery?
  • What are some of the components of a data analytics lifecycle that are resistant to agile or iterative development?
  • With the continued rise in the use of machine learning in production, how does that change the requirements for delivery and maintenance of an analytics platform?
  • What are some of the trends that you are most excited for in the analytics and data platform space?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Deep Learning For Data Engineers - Episode 71

Summary

Deep learning is the latest class of technology that is gaining widespread interest. As data engineers we are responsible for building and managing the platforms that power these models. To help us understand what is involved, we are joined this week by Thomas Henson. In this episode he shares his experiences experimenting with deep learning, what data engineers need to know about the infrastructure and data requirements to power the models that your team is building, and how it can be used to supercharge our ETL pipelines.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to dataengineeringpodcast.com/stratacon and dataengineeringpodcast.com/aicon to register today and get 20% off
  • Your host is Tobias Macey and today I’m interviewing Thomas Henson about what data engineers need to know about deep learning, including how to use it for their own projects

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by giving an overview of what deep learning is for anyone who isn’t familiar with it?
  • What has been your personal experience with deep learning and what set you down that path?
  • What is involved in building a data pipeline and production infrastructure for a deep learning product?
    • How does that differ from other types of analytics projects such as data warehousing or traditional ML?
  • For anyone who is in the early stages of a deep learning project, what are some of the edge cases or gotchas that they should be aware of?
  • What are your opinions on the level of involvement/understanding that data engineers should have with the analytical products that are being built with the information we collect and curate?
  • What are some ways that we can use deep learning as part of the data management process?
    • How does that shift the infrastructure requirements for our platforms?
  • Cloud providers have been releasing numerous products to provide deep learning and/or GPUs as a managed platform. What are your thoughts on that layer of the build vs buy decision?
  • What is your litmus test for whether to use deep learning vs explicit ML algorithms or a basic decision tree?
    • Deep learning algorithms are often a black box in terms of how decisions are made, however regulations such as GDPR are introducing requirements to explain how a given decision gets made. How does that factor into determining what approach to take for a given project?
  • For anyone who wants to learn more about deep learning, what are some resources that you recommend?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA