Open Source

Build Your Data Analytics Like An Engineer - Episode 81

Summary

In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse.

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!
  • 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 Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what DBT is and your motivation for creating it?
  • Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline?
  • Can you talk through the workflow for someone using DBT?
  • One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented?
  • The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented?
    • Are these packages driven by Fishtown Analytics or the dbt community?
  • What are the limitations of modeling everything as a SELECT statement?
  • Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings?
    • What are your thoughts on higher level approaches to SQL that compile down to the specific statements?
  • Can you explain how DBT is implemented and how the design has evolved since you first began working on it?
  • What are some of the features of DBT that are often overlooked which you find particularly useful?
  • What are some of the most interesting/unexpected/innovative ways that you have seen DBT used?
  • What are the additional features that the commercial version of DBT provides?
  • What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT?
  • When is it the wrong choice?
  • What do you have planned for the future of DBT?

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

Using FoundationDB As The Bedrock For Your Distributed Systems - Episode 80

Summary

The database market continues to expand, offering systems that are suited to virtually every use case. But what happens if you need something customized to your application? FoundationDB is a distributed key-value store that provides the primitives that you need to build a custom database platform. In this episode Ryan Worl explains how it is architected, how to use it for your applications, and provides examples of system design patterns that can be built on top of it. If you need a foundation for your distributed systems, then FoundationDB is definitely worth a closer look.

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 Ryan Worl about FoundationDB, a distributed key/value store that gives you the power of ACID transactions in a NoSQL database

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you explain what FoundationDB is and how you got involved with the project?
  • What are some of the unique use cases that FoundationDB enables?
  • Can you describe how FoundationDB is architected?
    • How is the ACID compliance implemented at the cluster level?
  • What are some of the mechanisms built into FoundationDB that contribute to its fault tolerance?
    • How are conflicts managed?
  • FoundationDB has an interesting feature in the form of Layers that provide different semantics on the underlying storage. Can you describe how that is implemented and some of the interesting layers that are available?
    • Is it possible to apply different layers, such as relational and document, to the same underlying objects in storage?
  • One of the aspects of FoundationDB that is called out in the documentation and which I have heard about elsewhere is the performance that it provides. Can you describe some of the implementation mechanics of FoundationDB that allow it to provide such high throughput?
  • For someone who wants to run FoundationDB can you describe a typical deployment topology?
    • What are the scaling factors for the underlying storage and for the Layers that are operating on the cluster?
  • Once you have a cluster deployed, what are some of the edge cases that users should watch out for?
    • How are version upgrades managed in a cluster?
  • What are some of the ways that FoundationDB impacts the way that an application developer or data engineer would architect their software as compared to working with something like Postgres or MongoDB?
  • What are some of the more interesting/unusual/unexpected ways that you have seen FoundationDB used?
  • When is FoundationDB the wrong choice?
  • What is in store for the future of FoundationDB?

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

Running Your Database On Kubernetes With KubeDB - Episode 79

Summary

Kubernetes is a driving force in the renaissance around deploying and running applications. However, managing the database layer is still a separate concern. The KubeDB project was created as a way of providing a simple mechanism for running your storage system in the same platform as your application. In this episode Tamal Saha explains how the KubeDB project got started, why you might want to run your database with Kubernetes, and how to get started. He also covers some of the challenges of managing stateful services in Kubernetes and how the fast pace of the community has contributed to the evolution of KubeDB. If you are at any stage of a Kubernetes implementation, or just thinking about it, this is definitely worth a listen to get some perspective on how to leverage it for your entire application stack.

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 Tamal Saha about KubeDB, a project focused on making running production-grade databases easy on Kubernetes

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what KubeDB is and how the project got started?
  • What are the main challenges associated with running a stateful system on top of Kubernetes?
    • Why would someone want to run their database on a container platform rather than on a dedicated instance or with a hosted service?
  • Can you describe how KubeDB is implemented and how that has evolved since you first started working on it?
  • Can you talk through how KubeDB simplifies the process of deploying and maintaining databases?
  • What is involved in adding support for a new database?
    • How do the requirements change for systems that are natively clustered?
  • How does KubeDB help with maintenance processes around upgrading existing databases to newer versions?
  • How does the work that you are doing on KubeDB compare to what is available in StorageOS?
    • Are there any other projects that are targeting similar goals?
  • What have you found to be the most interesting/challenging/unexpected aspects of building KubeDB?
  • What do you have planned for the future of the project?

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

The Alluxio Distributed Storage System - Episode 70

Summary

Distributed storage systems are the foundational layer of any big data stack. There are a variety of implementations which support different specialized use cases and come with associated tradeoffs. Alluxio is a distributed virtual filesystem which integrates with multiple persistent storage systems to provide a scalable, in-memory storage layer for scaling computational workloads independent of the size of your data. In this episode Bin Fan explains how he got involved with the project, how it is implemented, and the use cases that it is particularly well suited for. If your storage and compute layers are too tightly coupled and you want to scale them independently then Alluxio is the tool for the job.

Introduction

  • 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 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.
  • 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
  • Your host is Tobias Macey and today I’m interviewing Bin Fan about Alluxio, a distributed virtual filesystem for unified access to disparate data sources

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Alluxio is and the history of the project?
    • What are some of the use cases that Alluxio enables?
  • How is Alluxio implemented and how has its architecture evolved over time?
    • What are some of the techniques that you use to mitigate the impact of latency, particularly when interfacing with storage systems across cloud providers and private data centers?
  • When dealing with large volumes of data over time it is often necessary to age out older records to cheaper storage. What capabilities does Alluxio provide for that lifecycle management?
  • What are some of the most complex or challenging aspects of providing a unified abstraction across disparate storage platforms?
    • What are the tradeoffs that are made to provide a single API across systems with varying capabilities?
  • Testing and verification of distributed systems is a complex undertaking. Can you describe the approach that you use to ensure proper functionality of Alluxio as part of the development and release process?
    • In order to allow for this large scale testing with any regularity it must be straightforward to deploy and configure Alluxio. What are some of the mechanisms that you have built into the platform to simplify the operational aspects?
  • Can you describe a typical system topology that incorporates Alluxio?
  • For someone planning a deployment of Alluxio, what should they be considering in terms of system requirements and deployment topologies?
    • What are some edge cases or operational complexities that they should be aware of?
  • What are some cases where Alluxio is the wrong choice?
    • What are some projects or products that provide a similar capability to Alluxio?
  • What do you have planned for the future of the Alluxio project and company?

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

Cleaning And Curating Open Data For Archaeology - Episode 68

Summary

Archaeologists collect and create a variety of data as part of their research and exploration. Open Context is a platform for cleaning, curating, and sharing this data. In this episode Eric Kansa describes how they process, clean, and normalize the data that they host, the challenges that they face with scaling ETL processes which require domain specific knowledge, and how the information contained in connections that they expose is being used for interesting projects.

Introduction

  • 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 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.
  • 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
  • Your host is Tobias Macey and today I’m interviewing Eric Kansa about Open Context, a platform for publishing, managing, and sharing research data

Interview

  • Introduction

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

    I did some database and GIS work for my dissertation in archaeology, back in the late 1990’s. I got frustrated at the lack of comparative data, and I got frustrated at all the work I put into creating data that nobody would likely use. So I decided to focus my energies in research data management.

  • Can you start by describing what Open Context is and how it started?

    Open Context is an open access data publishing service for archaeology. It started because we need better ways of dissminating structured data and digital media than is possible with conventional articles, books and reports.

  • What are your protocols for determining which data sets you will work with?

    Datasets need to come from research projects that meet the normal standards of professional conduct (laws, ethics, professional norms) articulated by archaeology’s professional societies.

  • What are some of the challenges unique to research data?

    • What are some of the unique requirements for processing, publishing, and archiving research data?

      You have to work on a shoe-string budget, essentially providing "public goods". Archaeologists typically don’t have much discretionary money available, and publishing and archiving data are not yet very common practices.

      Another issues is that it will take a long time to publish enough data to power many "meta-analyses" that draw upon many datasets. The issue is that lots of archaeological data describes very particular places and times. Because datasets can be so particularistic, finding data relevant to your interests can be hard. So, we face a monumental task in supplying enough data to satisfy many, many paricularistic interests.

  • How much education is necessary around your content licensing for researchers who are interested in publishing their data with you?

    We require use of Creative Commons licenses, and greatly encourage the CC-BY license or CC-Zero (public domain) to try to keep things simple and easy to understand.

  • Can you describe the system architecture that you use for Open Context?

    Open Context is a Django Python application, with a Postgres database and an Apache Solr index. It’s running on Google cloud services on a Debian linux.

  • What is the process for cleaning and formatting the data that you host?

    • How much domain expertise is necessary to ensure proper conversion of the source data?

      That’s one of the bottle necks. We have to do an ETL (extract transform load) on each dataset researchers submit for publication. Each dataset may need lots of cleaning and back and forth conversations with data creators.

    • Can you discuss the challenges that you face in maintaining a consistent ontology?

    • What pieces of metadata do you track for a given data set?

  • Can you speak to the average size of data sets that you manage and any approach that you use to optimize for cost of storage and processing capacity?

    • Can you walk through the lifecycle of a given data set?
  • Data archiving is a complicated and difficult endeavor due to issues pertaining to changing data formats and storage media, as well as repeatability of computing environments to generate and/or process them. Can you discuss the technical and procedural approaches that you take to address those challenges?

  • Once the data is stored you expose it for public use via a set of APIs which support linked data. Can you discuss any complexities that arise from needing to identify and expose interrelations between the data sets?

  • What are some of the most interesting uses you have seen of the data that is hosted on Open Context?

  • What have been some of the most interesting/useful/challenging lessons that you have learned while working on Open Context?

  • What are your goals for the future of Open Context?

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: The Timeseries Database Built For SQL And Scale - Episode 65

Summary

The past year has been an active one for the timeseries market. New products have been launched, more businesses have moved to streaming analytics, and the team at Timescale has been keeping busy. In this episode the TimescaleDB CEO Ajay Kulkarni and CTO Michael Freedman stop by to talk about their 1.0 release, how the use cases for timeseries data have proliferated, and how they are continuing to simplify the task of processing your time oriented events.

Introduction

  • 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 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.
  • 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
  • Your host is Tobias Macey and today I’m welcoming Ajay Kulkarni and Mike Freedman back to talk about how TimescaleDB has grown and changed over the past year

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you refresh our memory about what TimescaleDB is?
  • How has the market for timeseries databases changed since we last spoke?
  • What has changed in the focus and features of the TimescaleDB project and company?
  • Toward the end of 2018 you launched the 1.0 release of Timescale. What were your criteria for establishing that milestone?
    • What were the most challenging aspects of reaching that goal?
  • In terms of timeseries workloads, what are some of the factors that differ across varying use cases?
    • How do those differences impact the ways in which Timescale is used by the end user, and built by your team?
  • What are some of the initial assumptions that you made while first launching Timescale that have held true, and which have been disproven?
  • How have the improvements and new features in the recent releases of PostgreSQL impacted the Timescale product?
    • Have you been able to leverage some of the native improvements to simplify your implementation?
    • Are there any use cases for Timescale that would have been previously impractical in vanilla Postgres that would now be reasonable without the help of Timescale?
  • What is in store for the future of the Timescale product and organization?

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

Performing Fast Data Analytics Using Apache Kudu - Episode 64

Summary

The Hadoop platform is purpose built for processing large, slow moving data in long-running batch jobs. As the ecosystem around it has grown, so has the need for fast data analytics on fast moving data. To fill this need the Kudu project was created with a column oriented table format that was tuned for high volumes of writes and rapid query execution across those tables. For a perfect pairing, they made it easy to connect to the Impala SQL engine. In this episode Brock Noland and Jordan Birdsell from PhData explain how Kudu is architected, how it compares to other storage systems in the Hadoop orbit, and how to start integrating it into you analytics pipeline.

Preamble

  • 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 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.
  • 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
  • Your host is Tobias Macey and today I’m interviewing Brock Noland and Jordan Birdsell about Apache Kudu and how it is able to provide fast analytics on fast data in the Hadoop ecosystem

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Kudu is and the motivation for building it?
    • How does it fit into the Hadoop ecosystem?
    • How does it compare to the work being done on the Iceberg table format?
  • What are some of the common application and system design patterns that Kudu supports?
  • How is Kudu architected and how has it evolved over the life of the project?
  • There are many projects in and around the Hadoop ecosystem that rely on Zookeeper as a building block for consensus. What was the reasoning for using Raft in Kudu?
  • How does the storage layer in Kudu differ from what would be found in systems like Hive or HBase?
    • What are the implementation details in the Kudu storage interface that have had the greatest impact on its overall speed and performance?
  • A number of the projects built for large scale data processing were not initially built with a focus on operational simplicity. What are the features of Kudu that simplify deployment and management of production infrastructure?
  • What was the motivation for using C++ as the language target for Kudu?
    • If you were to start the project over today what would you do differently?
  • What are some situations where you would advise against using Kudu?
  • What have you found to be the most interesting/unexpected/challenging lessons learned in the process of building and maintaining Kudu?
  • What are you most excited about for the future of Kudu?

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

Simplifying Continuous Data Processing Using Stream Native Storage In Pravega with Tom Kaitchuck - Episode 63

Summary

As more companies and organizations are working to gain a real-time view of their business, they are increasingly turning to stream processing technologies to fullfill that need. However, the storage requirements for continuous, unbounded streams of data are markedly different than that of batch oriented workloads. To address this shortcoming the team at Dell EMC has created the open source Pravega project. In this episode Tom Kaitchuk explains how Pravega simplifies storage and processing of data streams, how it integrates with processing engines such as Flink, and the unique capabilities that it provides in the area of exactly once processing and transactions. And if you listen at approximately the half-way mark, you can hear as the hosts mind is blown by the possibilities of treating everything, including schema information, as a stream.

Preamble

  • 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 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.
  • 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
  • Your host is Tobias Macey and today I’m interviewing Tom Kaitchuck about Pravega, an open source data storage platform optimized for persistent streams

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Pravega is and the story behind it?
  • What are the use cases for Pravega and how does it fit into the data ecosystem?
    • How does it compare with systems such as Kafka and Pulsar for ingesting and persisting unbounded data?
  • How do you represent a stream on-disk?
    • What are the benefits of using this format for persisted streams?
  • One of the compelling aspects of Pravega is the automatic sharding and resource allocation for variations in data patterns. Can you describe how that operates and the benefits that it provides?
  • I am also intrigued by the automatic tiering of the persisted storage. How does that work and what options exist for managing the lifecycle of the data in the cluster?
  • For someone who wants to build an application on top of Pravega, what interfaces does it provide and what architectural patterns does it lend itself toward?
  • What are some of the unique system design patterns that are made possible by Pravega?
  • How is Pravega architected internally?
  • What is involved in integrating engines such as Spark, Flink, or Storm with Pravega?
  • A common challenge for streaming systems is exactly once semantics. How does Pravega approach that problem?
    • Does it have any special capabilities for simplifying processing of out-of-order events?
  • For someone planning a deployment of Pravega, what is involved in building and scaling a cluster?
    • What are some of the operational edge cases that users should be aware of?
  • What are some of the most interesting, useful, or challenging experiences that you have had while building Pravega?
  • What are some cases where you would recommend against using Pravega?
  • What is in store for the future of Pravega?

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

Continuously Query Your Time-Series Data Using PipelineDB with Derek Nelson and Usman Masood - Episode 62

Summary

Processing high velocity time-series data in real-time is a complex challenge. The team at PipelineDB has built a continuous query engine that simplifies the task of computing aggregates across incoming streams of events. In this episode Derek Nelson and Usman Masood explain how it is architected, strategies for designing your data flows, how to scale it up and out, and edge cases to be aware of.

Preamble

  • 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 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.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Usman Masood and Derek Nelson about PipelineDB, an open source continuous query engine for PostgreSQL

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what PipelineDB is and the motivation for creating it?
    • What are the major use cases that it enables?
    • What are some example applications that are uniquely well suited to the capabilities of PipelineDB?
  • What are the major concepts and components that users of PipelineDB should be familiar with?
  • Given the fact that it is a plugin for PostgreSQL, what level of compatibility exists between PipelineDB and other plugins such as Timescale and Citus?
  • What are some of the common patterns for populating data streams?
  • What are the options for scaling PipelineDB systems, both vertically and horizontally?
    • How much elasticity does the system support in terms of changing volumes of inbound data?
    • What are some of the limitations or edge cases that users should be aware of?
  • Given that inbound data is not persisted to disk, how do you guard against data loss?
    • Is it possible to archive the data in a stream, unaltered, to a separate destination table or other storage location?
    • Can a separate table be used as an input stream?
  • Since the data being processed by the continuous queries is potentially unbounded, how do you approach checkpointing or windowing the data in the continuous views?
  • What are some of the features that you have found to be the most useful which users might initially overlook?
  • What would be involved in generating an alert or notification on an aggregate output that was in some way anomalous?
  • What are some of the most challenging aspects of building continuous aggregates on unbounded data?
  • What have you found to be some of the most interesting, complex, or challenging aspects of building and maintaining PipelineDB?
  • What are some of the most interesting or unexpected ways that you have seen PipelineDB used?
  • When is PipelineDB the wrong choice?
  • What do you have planned for the future of PipelineDB now that you have hit the 1.0 milestone?

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