Summary
In this episode of the Data Engineering Podcast Chakravarthy Kotaru talks about scaling data operations through standardized platform offerings. From his roots as an Oracle developer to leading the data platform at a major online travel company, Chakravarthy shares insights on managing diverse database technologies and providing databases as a service to streamline operations. He explains how his team has transitioned from DevOps to a platform engineering approach, centralizing expertise and automating repetitive tasks with AWS Service Catalog. Join them as they discuss the challenges of migrating legacy systems, integrating AI and ML for automation, and the importance of organizational buy-in in driving data platform success.
Announcements
Parting Question
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
In this episode of the Data Engineering Podcast Chakravarthy Kotaru talks about scaling data operations through standardized platform offerings. From his roots as an Oracle developer to leading the data platform at a major online travel company, Chakravarthy shares insights on managing diverse database technologies and providing databases as a service to streamline operations. He explains how his team has transitioned from DevOps to a platform engineering approach, centralizing expertise and automating repetitive tasks with AWS Service Catalog. Join them as they discuss the challenges of migrating legacy systems, integrating AI and ML for automation, and the importance of organizational buy-in in driving data platform success.
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 Chakri Kotaru about scaling successful data operations through standardized platform offerings
- Introduction
- How did you get involved in the area of data management?
- Can you start by outlining the different ways that you have seen teams you work with fail due to lack of structure and opinionated design?
- Why NoSQL?
- Pairing different styles of NoSQL for different problems
- Useful patterns for each NoSQL style (document, column family, graph, etc.)
- Challenges in platform automation and scaling edge cases
- What challenges do you anticipate as a result of the new pressures as a result of AI applications?
- What are the most interesting, innovative, or unexpected ways that you have seen platform engineering practices applied to data systems?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data platform engineering?
- When is NoSQL the wrong choice?
- What do you have planned for the future of platform principles for enabling data teams/data applications?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA