The Data Stack Show cover image

The Data Stack Show

140: Stream Processing for Machine Learning with Davor Bonaci of DataStax

May 31, 2023
01:01:30

Highlights from this week’s conversation include:

  • Davor’s journey from Google and what he was building there (3:32)
  • How work in stream processing changed Davor’s journey (5:10)
  • Analytical predictive models and infrastructure (9:39)
  • How Kaskada serves as a recommendation engine with data (14:05)
  • Kaskada’s user experience as an event processing platform (20:06)
  • Enhancing typical feature store architecture to achieve better results (23:34)
  • What is needed to improve stream and batch processes (27:39)
  • Using another syntax instead of SQL (36:44)
  • DataStax acquiring Kaskada and what will come from that merger (40:24)
  • Operationalizing and democratizing ML (47:54)
  • Final thoughts and takeaways (56:04) 

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.

RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode