Building the Backend: Data Solutions that Power Leading Organizations
Welcome to the Building the Backend Podcast! We’re a data podcast focused on uncovering the data technologies, processes, and patterns that are driving today’s most successful companies. You will hear from data leaders sharing their knowledge and insights with what’s working and what’s not working for them. Our goal is to bring you valuable insights that will save you and your team time when building a modern data architecture in the cloud. Topics will span from big data, AI, ML, governance, visualizations, and best practices for enabling your organization to be data-driven. If you are a chief data officer, data architect, data engineer, data analyst, and those building the backend data solutions then HIT SUBSCRIBE!
Building the Backend: Data Solutions that Power Leading Organizations
The Analytics Engine for All Your Data with Justin Borgman @ Starburst
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Travis Lawrence
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Season 1
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Episode 41
In this episode we speak with Justin Borgman, Chairman & CEO at Starburst, which is based on open source Trino (formerly PrestoSQL) and was recently valued at $3.35 billion after securing their series D funding. In this episode we discuss convergence of DW’s / DL's, why data lakes fail and much much more.
Top 3 takeaways
- The data mesh architecture is gaining adoption more quickly in Europe due to GDPR.
- There were two main limitations of data lakes when comparing to DW’s, performance and CRUD operations. Performance has been resolved with query engines like Starburst and tools like Apache Iceberg, Apache Hudi and Delta Lake are starting to close the gap with CRUD operations.
- The principle of a single source of truth / storing everything in a single DL or DW is not always feasible or possible depending on regulations. Starburst is bridging that gap and enabling data mesh and data fabric architectures.