184: Kafka Streams and Operationalizing Event Driven Applications with Apurva Mehta of Responsive

The Data Stack Show

Highlights from this week’s conversation include:

• Apruva’s background in streaming technology (0:48)
• Developer experience and Kafka streams (2:47)
• Motivation to bootstrap a startup (4:09)
• Meeting the Confluent founders and early work at Confluent (6:59)
• Projects at Confluent and transition to engineering management (10:34)
• Overview of Responsive and event-driven applications (12:55)
• Defining event-driven applications (15:33)
• Importance of latency and state in event-driven applications (18:54)
• Low Latency and Stateful Processing (21:52)
• In-Memory Storage and Evolution of Kafka (25:02)
• Motivation for KSQL and Kafka Streams (29:46)
• Category Creation and Database-like Interface (34:33)
• Developer Experience with Kafka and Kafka Streams (38:50)
• Kafka Streams Functionality and Operational Challenges (41:44)
• Metrics and Tuning Configurations (43:33)
• Architecture and Decoupling in Kafka Streams (45:39)
• State Storage and Transition from RocksDB (47:48)
• Future of Event-Driven Architectures (56:30)
• Final thoughts and takeaways (57:36)

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.

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