Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way
Data Engineering Podcast
00:00
Machine Learning
The machine learning question is another thing that always adds complexity to the data platform requirements. The industry is thinking through concepts of the data mai, which is essentially getting data to be owned by domain owners and make sure that there is a strong ownership model established for data sets. That way, the data lic has kenetis two prong strategy, where there one side of it is dust, raw data that exists for anybody to explore and visualize. And then there's this other aspect where, let’s say, product teams or business teams curate specific data sets and essentially use the same stack.
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Summary
Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional layers of difficulty. Srivatsan Sridharan has had the opportunity to design, build, and run data lake platforms for both Yelp and Robinhood, with many valuable lessons learned from each experience. In this episode he shares his insights and advice on how to approach such an undertaking in your own organization.
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 Srivatsan Sridharan about the technological, staffing, and design considerations for building a data platform
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what your experience has been with designing and implementing data platforms?
- What are the elements that you have found to be common requirements across organizations and data characteristics?
- What are the architectural elements that require the most detailed consideration based on organizational needs and data requirements?
- How has the ecosystem for building maintainable and usable data lakes matured over the past few years?
- What are the elements that are still cumbersome or intractable?
- The streaming ecosystem has also gone through substantial changes over the past few years. What is your synopsis of the meaningful differences between todays options and where we were ~6 years ago?
- How did your experiences at Yelp inform your current architectural approach at Robinhood?
- Can you describe your current platform architecture?
- What are the primary capabilities that you are optimizing for?
- What is your evaluation process for determining what components to use in your platform?
- How do you approach the build vs. buy problem and quantify the tradeoffs?
- What are the most interesting, innovative, or unexpected ways that you have seen your data systems used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on designing and implementing data platforms across your career?
- When is a data lake architecture the wrong choice?
- What do you have planned for the future of the data platform at Robinhood?
Contact Info
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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
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Links
- Robinhood
- Yelp
- Kafka
- Spark
- Flink
- Pulsar
- Parquet
- Change Data Capture
- Delta Lake
- Hudi
- Redshift
- BigQuery
- Informatica
- Data Mesh
- PrestoDB
- Trino
- Airbyte
- Meltano
- Fivetran
- Stitch
- Pinot
- Clickhouse
- Druid
- Iceberg
- Looker
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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