Building Your Data Warehouse On Top Of PostgreSQL
Data Engineering Podcast
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Postgres' user defined functions and gaps in data management technology
This chapter explores the capability of Postgres to push user defined functions using different programming languages and also discusses the potential of pushing down machine learning into the database. It concludes with a discussion on the biggest gaps in data management technology, including the shortage of chips and the need for improved security tooling.
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Summary
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.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Thomas Richter and Joshua Drake about using Postgres as your data warehouse
Interview
- Introduction
- 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?
Contact Info
- Thomas
- JD
- @linuxhiker on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- PostgreSQL
- Swarm64
- Command Prompt Inc.
- IBM
- Cognos
- OLAP Cube
- MariaDB
- MySQL
- Powell’s Books
- DBase
- Practical PostgreSQL
- Netezza
- Presto
- Trino
- Apache Drill
- Parquet
- Parquet Foreign Data Wrapper
- Snowflake
- Amazon RDS
- Amazon Aurora
- Hyperscale
- Citus
- TimescaleDB
- Greenplum
- zedstore
- Redshift
- Microsoft SQL Server
- Postgres Tablespaces
- Debezium
- EDI == Enterprise Data Integration
- Change Data Capture
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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