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

Ajay

LinkedIn @acoustik on Twitter Timescale Blog

Mike

Website LinkedIn @michaelfreedman on Twitter Timescale Blog

Timescale

Website @timescaledb on Twitter GitHub

Parting Question

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

Links

Timescale PostGreSQL Citus Timescale Design Blog Post MIT NYU Stanford SDN Princeton Machine Data Timeseries Data List of Timeseries Databases NoSQL Online Transaction Processing (OLTP) Object Relational Mapper (ORM) Grafana Tableau Kafka When Boring Is Awesome PostGreSQL RDS Google Cloud SQL Azure DB Docker Continuous Aggregates Streaming Replication PGPool II Kubernetes Docker Swarm Citus Data

Website Data Engineering Podcast Interview

Database Indexing B-Tree Index GIN Index GIST Index STE Energy Redis Graphite Prometheus pg_prometheus OpenMetrics Standard Proposal Timescale Parallel Copy Hadoop PostGIS KDB+ DevOps Internet of Things MongoDB Elastic DataBricks Apache Spark Confluent New Enterprise Associates MapD Benchmark Ventures Hortonworks 2σ Ventures CockroachDB Cloudflare EMC Timescale Blog: Why SQL is beating NoSQL, and what this means for the future of data

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Summary With the wealth of formats for sending and storing data it can be difficult to determine which one to use. In this episode Doug Cutting, creator of Avro, and Julien Le Dem, creator of Parquet, dig into the different classes of serialization formats, what their strengths are, and how to choose one for your workload. They also discuss the role of Arrow as a mechanism for in-memory data sharing and how hardware evolution will influence the state of the art for data formats.

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 This is your host Tobias Macey and today I’m interviewing Julien Le Dem and Doug Cutting about data serialization formats and how to pick the right one for your systems.

Interview

Introduction How did you first get involved in the area of data management? What are the main serialization formats used for data storage and analysis? What are the tradeoffs that are offered by the different formats? How have the different storage and analysis tools influenced the types of storage formats that are available? You’ve each developed a new on-disk data format, Avro and Parquet respectively. What were your motivations for investing that time and effort? Why is it important for data engineers to carefully consider the format in which they transfer their data between systems?

What are the switching costs involved in moving from one format to another after you have started using it in a production system?

What are some of the new or upcoming formats that you are each excited about? How do you anticipate the evolving hardware, patterns, and tools for processing data to influence the types of storage formats that maintain or grow their popularity?

Contact Information

Doug:

cutting on GitHub Blog @cutting on Twitter

Julien

Email @J_ on Twitter Blog julienledem on GitHub

Links

Apache Avro Apache Parquet Apache Arrow Hadoop Apache Pig Xerox Parc Excite Nutch Vertica Dremel White Paper

Twitter Blog on Release of Parquet

CSV XML Hive Impala Presto Spark SQL Brotli ZStandard Apache Drill Trevni Apache Calcite

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

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Summary

Building a data pipeline that is reliable and flexible is a difficult task, especially when you have a small team. Astronomer is a platform that lets you skip straight to processing your valuable business data. Ry Walker, the CEO of Astronomer, explains how the company got started, how the platform works, and their commitment to open source.

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 www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss 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 This is your host Tobias Macey and today I’m interviewing Ry Walker, CEO of Astronomer, the platform for data engineering.

Interview

Introduction How did you first get involved in the area of data management? What is Astronomer and how did it get started? Regulatory challenges of processing other people’s data What does your data pipelining architecture look like? What are the most challenging aspects of building a general purpose data management environment? What are some of the most significant sources of technical debt in your platform? Can you share some of the failures that you have encountered while architecting or building your platform and company and how you overcame them? There are certain areas of the overall data engineering workflow that are well defined and have numerous tools to choose from. What are some of the unsolved problems in data management? What are some of the most interesting or unexpected uses of your platform that you are aware of?

Contact Information

Email @rywalker on Twitter

Links

Astronomer Kiss Metrics Segment Marketing tools chart Clickstream HIPAA FERPA PCI Mesos Mesos DC/OS Airflow SSIS Marathon Prometheus Grafana Terraform Kafka Spark ELK Stack React GraphQL PostGreSQL MongoDB Ceph Druid Aries Vault Adapter Pattern Docker Kinesis API Gateway Kong AWS Lambda Flink Redshift NOAA Informatica SnapLogic Meteor

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Summary

There is a vast constellation of tools and platforms for processing and analyzing your data. In this episode Matthew Rocklin talks about how Dask fills the gap between a task oriented workflow tool and an in memory processing framework, and how it brings the power of Python to bear on the problem of big data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure 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 Matthew Rocklin about Dask and the Blaze ecosystem.

Interview with Matthew Rocklin

Introduction How did you get involved in the area of data engineering? Dask began its life as part of the Blaze project. Can you start by describing what Dask is and how it originated? There are a vast number of tools in the field of data analytics. What are some of the specific use cases that Dask was built for that weren’t able to be solved by the existing options? One of the compelling features of Dask is the fact that it is a Python library that allows for distributed computation at a scale that has largely been the exclusive domain of tools in the Hadoop ecosystem. Why do you think that the JVM has been the reigning platform in the data analytics space for so long? Do you consider Dask, along with the larger Blaze ecosystem, to be a competitor to the Hadoop ecosystem, either now or in the future? Are you seeing many Hadoop or Spark solutions being migrated to Dask? If so, what are the common reasons? There is a strong focus for using Dask as a tool for interactive exploration of data. How does it compare to something like Apache Drill? For anyone looking to integrate Dask into an existing code base that is already using NumPy or Pandas, what does that process look like? How do the task graph capabilities compare to something like Airflow or Luigi? Looking through the documentation for the graph specification in Dask, it appears that there is the potential to introduce cycles or other bugs into a large or complex task chain. Is there any built-in tooling to check for that before submitting the graph for execution? What are some of the most interesting or unexpected projects that you have seen Dask used for? What do you perceive as being the most relevant aspects of Dask for data engineering/data infrastructure practitioners, as compared to the end users of the systems that they support? What are some of the most significant problems that you have been faced with, and which still need to be overcome in the Dask project? I know that the work on Dask is largely performed under the umbrella of PyData and sponsored by Continuum Analytics. What are your thoughts on the financial landscape for open source data analytics and distributed computation frameworks as compared to the broader world of open source projects?

Keep in touch

@mrocklin on Twitter mrocklin on GitHub

Links

http://matthewrocklin.com/blog/work/2016/09/22/cluster-deployments?utm_source=rss&utm_medium=rss https://opendatascience.com/blog/dask-for-institutions/?utm_source=rss&utm_medium=rss Continuum Analytics 2sigma X-Array Tornado

Website Podcast Interview

Airflow Luigi Mesos Kubernetes Spark Dryad Yarn Read The Docs XData

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