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

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.