There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. If you want to build a warehouse that gives you both control and flexibility then you might consider building on top of the venerable PostgreSQL project. In this episode Thomas Richter and Joshua Drake share their advice on how to build a production ready data warehouse with Postgres.
Firebolt is the world’s fastest cloud data warehouse, purpose-built for high performance analytics. It provides orders of magnitude faster query performance at a fraction of the cost compared to alternatives. Companies that adopted Firebolt have been able to deploy data warehouses in weeks and deliver sub-second performance at terabyte to petabyte scale for a wide range of interactive, high performance analytics across internal BI as well as customer facing analytics use cases. Visit dataengineeringpodcast.com/firebolt to get started.
Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more.
Go to dataengineeringpodcast.com/atlan and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- 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 their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt.
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- Your host is Tobias Macey and today I’m interviewing Thomas Richter and Joshua Drake about using Postgres as your data warehouse
- How did you get involved in the area of data management?
- Can you start by establishing a working definition of what constitutes a data warehouse for the purpose of this discussion?
- What are the limitations for out-of-the-box Postgres when trying to use it for these workloads?
- There are a large and growing number of options for data warehouse style workloads. How would you categorize the different systems and what is PostgreSQL’s position in that ecosystem?
- What do you see as the motivating factors for a team or organization to select from among those categories?
- Why would someone want to use Postgres as their data warehouse platform rather than using a purpose-built engine?
- What is the cost/performance equation for Postgres as compared to other data warehouse solutions?
- For someone who wants to turn Postgres into a data warehouse engine, what are their options?
- What are the relative tradeoffs of the different open source and commercial offerings? (e.g. Citus, cstore_fdw, zedstore, Swarm64, Greenplum, etc.)
- One of the biggest areas of growth right now is in the "cloud data warehouse" market where storage and compute are decoupled. What are the options for making that possible with Postgres? (e.g. using foreign data wrappers for interacting with data lake storage (S3, HDFS, Alluxio, etc.))
- What areas of work are happening in the Postgres community for upcoming releases to make it more easily suited to data warehouse/analytical workloads?
- What are some of the most interesting, innovative, or unexpected ways that you have seen Postgres used in analytical contexts?
- What are the most interesting, unexpected, or challenging lessons that you have learned from your own experiences of building analytical systems with Postgres?
- When is Postgres the wrong choice for a data warehouse?
- What are you most excited for/what are you keeping an eye on in upcoming releases of Postgres and its ecosystem?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Command Prompt Inc.
- OLAP Cube
- Powell’s Books
- Practical PostgreSQL
- Apache Drill
- Parquet Foreign Data Wrapper
- Amazon RDS
- Amazon Aurora
- Microsoft SQL Server
- Postgres Tablespaces
- EDI == Enterprise Data Integration
- Change Data Capture