Data Infrastructure

ThreatStack: Data Driven Cloud Security with Pete Cheslock and Patrick Cable - Episode 25

Summary

Cloud computing and ubiquitous virtualization have changed the ways that our applications are built and deployed. This new environment requires a new way of tracking and addressing the security of our systems. ThreatStack is a platform that collects all of the data that your servers generate and monitors for unexpected anomalies in behavior that would indicate a breach and notifies you in near-realtime. In this episode ThreatStack’s director of operations, Pete Cheslock, and senior infrastructure security engineer, Patrick Cable, discuss the data infrastructure that supports their platform, how they capture and process the data from client systems, and how that information can be used to keep your systems safe from attackers.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • Your host is Tobias Macey and today I’m interviewing Pete Cheslock and Pat Cable about the data infrastructure and security controls at ThreatStack

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Why don’t you start by explaining what ThreatStack does?
    • What was lacking in the existing options (services and self-hosted/open source) that ThreatStack solves for?
  • Can you describe the type(s) of data that you collect and how it is structured?
  • What is the high level data infrastructure that you use for ingesting, storing, and analyzing your customer data?
    • How do you ensure a consistent format of the information that you receive?
    • How do you ensure that the various pieces of your platform are deployed using the proper configurations and operating as intended?
    • How much configuration do you provide to the end user in terms of the captured data, such as sampling rate or additional context?
  • I understand that your original architecture used RabbitMQ as your ingest mechanism, which you then migrated to Kafka. What was your initial motivation for that change?
    • How much of a benefit has that been in terms of overall complexity and cost (both time and infrastructure)?
  • How do you ensure the security and provenance of the data that you collect as it traverses your infrastructure?
  • What are some of the most common vulnerabilities that you detect in your client’s infrastructure?
  • For someone who wants to start using ThreatStack, what does the setup process look like?
  • What have you found to be the most challenging aspects of building and managing the data processes in your environment?
  • What are some of the projects that you have planned to improve the capacity or capabilities of your infrastructure?

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

MarketStore: Managing Timeseries Financial Data with Hitoshi Harada and Christopher Ryan - Episode 24

Summary

The data that is used in financial markets is time oriented and multidimensional, which makes it difficult to manage in either relational or timeseries databases. To make this information more manageable the team at Alapaca built a new data store specifically for retrieving and analyzing data generated by trading markets. In this episode Hitoshi Harada, the CTO of Alapaca, and Christopher Ryan, their lead software engineer, explain their motivation for building MarketStore, how it operates, and how it has helped to simplify their development workflows.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • Your host is Tobias Macey and today I’m interviewing Christopher Ryan and Hitoshi Harada about MarketStore, a storage server for large volumes of financial timeseries data

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What was your motivation for creating MarketStore?
  • What are the characteristics of financial time series data that make it challenging to manage?
  • What are some of the workflows that MarketStore is used for at Alpaca and how were they managed before it was available?
  • With MarketStore’s data coming from multiple third party services, how are you managing to keep the DB up-to-date and in sync with those services?
    • What is the worst case scenario if there is a total failure in the data store?
    • What guards have you built to prevent such a situation from occurring?
  • Since MarketStore is used for querying and analyzing data having to do with financial markets and there are potentially large quantities of money being staked on the results of that analysis, how do you ensure that the operations being performed in MarketStore are accurate and repeatable?
  • What were the most challenging aspects of building MarketStore and integrating it into the rest of your systems?
  • Motivation for open sourcing the code?
  • What is the next planned major feature for MarketStore, and what use-case is it aiming to support?

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

Stretching The Elastic Stack with Philipp Krenn - Episode 23

Summary

Search is a common requirement for applications of all varieties. Elasticsearch was built to make it easy to include search functionality in projects built in any language. From that foundation, the rest of the Elastic Stack has been built, expanding to many more use cases in the proces. In this episode Philipp Krenn describes the various pieces of the stack, how they fit together, and how you can use them in your infrastructure to store, search, and analyze your data.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • Your host is Tobias Macey and today I’m interviewing Philipp Krenn about the Elastic Stack and the ways that you can use it in your systems

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • The Elasticsearch product has been around for a long time and is widely known, but can you give a brief overview of the other components that make up the Elastic Stack and how they work together?
  • Beyond the common pattern of using Elasticsearch as a search engine connected to a web application, what are some of the other use cases for the various pieces of the stack?
  • What are the common scaling bottlenecks that users should be aware of when they are dealing with large volumes of data?
  • What do you consider to be the biggest competition to the Elastic Stack as you expand the capabilities and target usage patterns?
  • What are the biggest challenges that you are tackling in the Elastic stack, technical or otherwise?
  • What are the biggest challenges facing Elastic as a company in the near to medium term?
  • Open source as a business model: https://www.elastic.co/blog/doubling-down-on-open
  • What is the vision for Elastic and the Elastic Stack going forward and what new features or functionality can we look forward to?

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

Database Refactoring Patterns with Pramod Sadalage - Episode 22

Summary

As software lifecycles move faster, the database needs to be able to keep up. Practices such as version controlled migration scripts and iterative schema evolution provide the necessary mechanisms to ensure that your data layer is as agile as your application. Pramod Sadalage saw the need for these capabilities during the early days of the introduction of modern development practices and co-authored a book to codify a large number of patterns to aid practitioners, and in this episode he reflects on the current state of affairs and how things have changed over the past 12 years.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host is Tobias Macey and today I’m interviewing Pramod Sadalage about refactoring databases and integrating database design into an iterative development workflow

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • You first co-authored Refactoring Databases in 2006. What was the state of software and database system development at the time and why did you find it necessary to write a book on this subject?
  • What are the characteristics of a database that make them more difficult to manage in an iterative context?
  • How does the practice of refactoring in the context of a database compare to that of software?
  • How has the prevalence of data abstractions such as ORMs or ODMs impacted the practice of schema design and evolution?
  • Is there a difference in strategy when refactoring the data layer of a system when using a non-relational storage system?
  • How has the DevOps movement and the increased focus on automation affected the state of the art in database versioning and evolution?
  • What have you found to be the most problematic aspects of databases when trying to evolve the functionality of a system?
  • Looking back over the past 12 years, what has changed in the areas of database design and evolution?
    • How has the landscape of tooling for managing and applying database versioning changed since you first wrote Refactoring Databases?
    • What do you see as the biggest challenges facing us over the next few years?

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

The Future Data Economy with Roger Chen - Episode 21

Summary

Data is an increasingly sought after raw material for business in the modern economy. One of the factors driving this trend is the increase in applications for machine learning and AI which require large quantities of information to work from. As the demand for data becomes more widespread the market for providing it will begin transform the ways that information is collected and shared among and between organizations. With his experience as a chair for the O’Reilly AI conference and an investor for data driven businesses Roger Chen is well versed in the challenges and solutions being facing us. In this episode he shares his perspective on the ways that businesses can work together to create shared data resources that will allow them to reduce the redundancy of their foundational data and improve their overall effectiveness in collecting useful training sets for their particular products.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • A few announcements:
    • The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20%
    • If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.
  • Your host is Tobias Macey and today I’m interviewing Roger Chen about data liquidity and its impact on our future economies

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • You wrote an essay discussing how the increasing usage of machine learning and artificial intelligence applications will result in a demand for data that necessitates what you refer to as ‘Data Liquidity’. Can you explain what you mean by that term?
  • What are some examples of the types of data that you envision as being foundational to multiple organizations and problem domains?
  • Can you provide some examples of the structures that could be created to facilitate data sharing across organizational boundaries?
  • Many companies view their data as a strategic asset and are therefore loathe to provide access to other individuals or organizations. What encouragement can you provide that would convince them to externalize any of that information?
  • What kinds of storage and transmission infrastructure and tooling are necessary to allow for wider distribution of, and collaboration on, data assets?
  • What do you view as being the privacy implications from creating and sharing these larger pools of data inventory?
  • What do you view as some of the technical challenges associated with identifying and separating shared data from those that are specific to the business model of the organization?
  • With broader access to large data sets, how do you anticipate that impacting the types of businesses or products that are possible for smaller organizations?

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

Honeycomb Data Infrastructure with Sam Stokes - Episode 20

Summary

One of the sources of data that often gets overlooked is the systems that we use to run our businesses. This data is not used to directly provide value to customers or understand the functioning of the business, but it is still a critical component of a successful system. Sam Stokes is an engineer at Honeycomb where he helps to build a platform that is able to capture all of the events and context that occur in our production environments and use them to answer all of your questions about what is happening in your system right now. In this episode he discusses the challenges inherent in capturing and analyzing event data, the tools that his team is using to make it possible, and how this type of knowledge can be used to improve your critical infrastructure.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • A few announcements:
    • There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20%
    • The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20%
    • If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.
  • Your host is Tobias Macey and today I’m interviewing Sam Stokes about his work at Honeycomb, a modern platform for observability of software systems

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What is Honeycomb and how did you get started at the company?
  • Can you start by giving an overview of your data infrastructure and the path that an event takes from ingest to graph?
  • What are the characteristics of the event data that you are dealing with and what challenges does it pose in terms of processing it at scale?
  • In addition to the complexities of ingesting and storing data with a high degree of cardinality, being able to quickly analyze it for customer reporting poses a number of difficulties. Can you explain how you have built your systems to facilitate highly interactive usage patterns?
  • A high degree of visibility into a running system is desirable for developers and systems adminstrators, but they are not always willing or able to invest the effort to fully instrument the code or servers that they want to track. What have you found to be the most difficult aspects of data collection, and do you have any tooling to simplify the implementation for user?
  • How does Honeycomb compare to other systems that are available off the shelf or as a service, and when is it not the right tool?
  • What have been some of the most challenging aspects of building, scaling, and marketing Honeycomb?

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

TimescaleDB: Fast And Scalable Timeseries with Ajay Kulkarni and Mike Freedman - Episode 18

Summary

As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host is Tobias Macey and today I’m interviewing Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Timescale is and how the project got started?
  • The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options?
  • In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices?
  • How is Timescale implemented and how has the internal architecture evolved since you first started working on it?
    • What impact has the 10.0 release of PostGreSQL had on the design of the project?
    • Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL?
  • For someone who wants to start using Timescale what is involved in deploying and maintaining it?
  • What are the axes for scaling Timescale and what are the points where that scalability breaks down?
    • Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances?
  • What has been the most challenging aspect of building and marketing Timescale?
  • When is Timescale the wrong tool to use for time series data?
  • One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus?
  • What are some of the most interesting uses of Timescale that you have seen?
  • Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health?
  • What features or improvements do you have planned for future releases of Timescale?

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

Pulsar: Fast And Scalable Messaging with Rajan Dhabalia and Matteo Merli - Episode 17

Summary

One of the critical components for modern data infrastructure is a scalable and reliable messaging system. Publish-subscribe systems have been popular for many years, and recently stream oriented systems such as Kafka have been rising in prominence. This week Rajan Dhabalia and Matteo Merli discuss the work they have done on Pulsar, which supports both options, in addition to being globally scalable and fast. They explain how Pulsar is architected, how to scale it, and how it fits into your existing infrastructure.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • A few announcements:
    • There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20%
    • The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20%
    • If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.
  • Your host is Tobias Macey and today I’m interviewing Rajan Dhabalia and Matteo Merli about Pulsar, a distributed open source pub-sub messaging system

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Pulsar is and what the original inspiration for the project was?
  • What have been some of the most challenging aspects of building and promoting Pulsar?
  • For someone who wants to run Pulsar, what are the infrastructure and network requirements that they should be considering and what is involved in deploying the various components?
  • What are the scaling factors for Pulsar and what aspects of deployment and administration should users pay special attention to?
  • What projects or services do you consider to be competitors to Pulsar and what makes it stand out in comparison?
  • The documentation mentions that there is an API layer that provides drop-in compatibility with Kafka. Does that extend to also supporting some of the plugins that have developed on top of Kafka?
  • One of the popular aspects of Kafka is the persistence of the message log, so I’m curious how Pulsar manages long-term storage and reprocessing of messages that have already been acknowledged?
  • When is Pulsar the wrong tool to use?
  • What are some of the improvements or new features that you have planned for the future of Pulsar?

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

Dat: Distributed Versioned Data Sharing with Danielle Robinson and Joe Hand - Episode 16

Summary

Sharing data across multiple computers, particularly when it is large and changing, is a difficult problem to solve. In order to provide a simpler way to distribute and version data sets among collaborators the Dat Project was created. In this episode Danielle Robinson and Joe Hand explain how the project got started, how it functions, and some of the many ways that it can be used. They also explain the plans that the team has for upcoming features and uses that you can watch out for in future releases.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
  • Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • You can help support the show by checking out the Patreon page which is linked from the site.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • A few announcements:
    • There is still time to register for the O’Reilly Strata Conference in San Jose, CA March 5th-8th. Use the link dataengineeringpodcast.com/strata-san-jose to register and save 20%
    • The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20%
    • If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.
  • Your host is Tobias Macey and today I’m interviewing Danielle Robinson and Joe Hand about Dat Project, a distributed data sharing protocol for building applications of the future

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What is the Dat project and how did it get started?
  • How have the grants to the Dat project influenced the focus and pace of development that was possible?
    • Now that you have established a non-profit organization around Dat, what are your plans to support future sustainability and growth of the project?
  • Can you explain how the Dat protocol is designed and how it has evolved since it was first started?
  • How does Dat manage conflict resolution and data versioning when replicating between multiple machines?
  • One of the primary use cases that is mentioned in the documentation and website for Dat is that of hosting and distributing open data sets, with a focus on researchers. How does Dat help with that effort and what improvements does it offer over other existing solutions?
  • One of the difficult aspects of building a peer-to-peer protocol is that of establishing a critical mass of users to add value to the network. How have you approached that effort and how much progress do you feel that you have made?
  • How does the peer-to-peer nature of the platform affect the architectural patterns for people wanting to build applications that are delivered via dat, vs the common three-tier architecture oriented around persistent databases?
  • What mechanisms are available for content discovery, given the fact that Dat URLs are private and unguessable by default?
  • For someone who wants to start using Dat today, what is involved in creating and/or consuming content that is available on the network?
  • What have been the most challenging aspects of building and promoting Dat?
  • What are some of the most interesting or inspiring uses of the Dat protocol that you are aware of?

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

CRDTs and Distributed Consensus with Christopher Meiklejohn - Episode 14

Summary

As we scale our systems to handle larger volumes of data, geographically distributed users, and varied data sources the requirement to distribute the computational resources for managing that information becomes more pronounced. In order to ensure that all of the distributed nodes in our systems agree with each other we need to build mechanisms to properly handle replication of data and conflict resolution. In this episode Christopher Meiklejohn discusses the research he is doing with Conflict-Free Replicated Data Types (CRDTs) and how they fit in with existing methods for sharing and sharding data. He also shares resources for systems that leverage CRDTs, how you can incorporate them into your systems, and when they might not be the right solution. It is a fascinating and informative treatment of a topic that is becoming increasingly relevant in a data driven world.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
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  • Your host is Tobias Macey and today I’m interviewing Christopher Meiklejohn about establishing consensus in distributed systems

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • You have dealt with CRDTs with your work in industry, as well as in your research. Can you start by explaining what a CRDT is, how you first began working with them, and some of their current manifestations?
  • Other than CRDTs, what are some of the methods for establishing consensus across nodes in a system and how does increased scale affect their relative effectiveness?
  • One of the projects that you have been involved in which relies on CRDTs is LASP. Can you describe what LASP is and what your role in the project has been?
  • Can you provide examples of some production systems or available tools that are leveraging CRDTs?
  • If someone wants to take advantage of CRDTs in their applications or data processing, what are the available off-the-shelf options, and what would be involved in implementing custom data types?
  • What areas of research are you most excited about right now?
  • Given that you are currently working on your PhD, do you have any thoughts on the projects or industries that you would like to be involved in once your degree is completed?

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