Real Time

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.
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  • 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

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

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

Wallaroo with Sean T. Allen - Episode 12

Summary

Data oriented applications that need to operate on large, fast-moving sterams of information can be difficult to build and scale due to the need to manage their state. In this episode Sean T. Allen, VP of engineering for Wallaroo Labs, explains how Wallaroo was designed and built to reduce the cognitive overhead of building this style of project. He explains the motivation for building Wallaroo, how it is implemented, and how you can start using it today.

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
  • Your host is Tobias Macey and today I’m interviewing Sean T. Allen about Wallaroo, a framework for building and operating stateful data applications at scale

Interview

  • Introduction
  • How did you get involved in the area of data engineering?
  • What is Wallaroo and how did the project get started?
  • What is the Pony language, and what features does it have that make it well suited for the problem area that you are focusing on?
  • Why did you choose to focus first on Python as the language for interacting with Wallaroo and how is that integration implemented?
  • How is Wallaroo architected internally to allow for distributed state management?
    • Is the state persistent, or is it only maintained long enough to complete the desired computation?
    • If so, what format do you use for long term storage of the data?
  • What have been the most challenging aspects of building the Wallaroo platform?
  • Which axes of the CAP theorem have you optimized for?
  • For someone who wants to build an application on top of Wallaroo, what is involved in getting started?
  • Once you have a working application, what resources are necessary for deploying to production and what are the scaling factors?
    • What are the failure modes that users of Wallaroo need to account for in their application or infrastructure?
  • What are some situations or problem types for which Wallaroo would be the wrong choice?
  • What are some of the most interesting or unexpected uses of Wallaroo that you have seen?
  • What do you have planned for the future of Wallaroo?

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

Buzzfeed Data Infrastructure with Walter Menendez - Episode 7

Summary

Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • 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
  • Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for?
  • What are some of the types of data inputs and outputs that you work with at Buzzfeed?
  • Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision?
  • What does the architecture of your data platform look like and what are some of the most significant areas of technical debt?
  • Which platforms and languages are most widely leveraged in your team and what are some of the outliers?
  • What are some of the most significant challenges that you face, both technically and organizationally?
  • What are some of the dead ends that you have run into or failed projects that you have tried?
  • What has been the most successful project that you have completed and how do you measure that success?

Contact Info

Links

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