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The Analytics Engine for All Your Data with Justin Borgman @ Starburst

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Manage episode 322817537 series 2881014
Innehåll tillhandahållet av Travis Lawrence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Travis Lawrence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

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.
  continue reading

43 episoder

Artwork
iconDela
 
Manage episode 322817537 series 2881014
Innehåll tillhandahållet av Travis Lawrence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Travis Lawrence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

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.
  continue reading

43 episoder

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