Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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28 August 2023

Building An Internal Database As A Service Platform At Cloudflare - E389

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Data persistence is one of the most challenging aspects of computer systems. In the era of the cloud most developers rely on hosted services to manage their databases, but what if you are a cloud service? In this episode Vignesh Ravichandran explains how his team at Cloudflare provides PostgreSQL as a service to their developers for low latency and high uptime services at global scale. This is an interesting and insightful look at pragmatic engineering for reliability and scale.


  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting
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  • Your host is Tobias Macey and today I'm interviewing Vignesh Ravichandran about building an internal database as a service platform at Cloudflare


  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing the different database workloads that you have at Cloudflare?
    • What are the different methods that you have used for managing database instances?
  • What are the requirements and constraints that you had to account for in designing your current system?
  • Why Postgres?
  • optimizations for Postgres
    • simplification from not supporting multiple engines
  • limitations in postgres that make multi-tenancy challenging
  • scale of operation (data volume, request rate
  • What are the most interesting, innovative, or unexpected ways that you have seen your DBaaS used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on your internal database platform?
  • When is an internal database as a service the wrong choice?
  • What do you have planned for the future of Postgres hosting at Cloudflare?

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Parting Question

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

Closing Announcements

  • Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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