Event-driven Utopia

Everything in Kafka is modeled around partitions. They rule Kafka’s storage, scalability, replication, and message movement.

What are event-driven APIs? How do they differ from REST APIs? What technology choices are there to build them?

How these two non-JVM based products make stream processing accessible for the masses by reducing the operational overhead

Use Apache Pinot to ingest fitness band events from a Kafka topic and make them available for immediate querying from a leaderboard web…

How to build an incrementally updated materialized view that serves queries in a faster and scalable manner?

How to build an incrementally updated materialized view that serves queries in a faster and scalable manner?

How to build a streaming data pipeline to capture MySQL database changes and stream them to Apache Pinot via Debezium and Kafka

How do speed, accuracy, and cost-effectiveness influence the choice between batch and streaming analytics?

Why modern streaming stack is replacing the classic streaming architecture? What’s the composition and what values it brings?

How Pinot solves the toughest problems in the data analytics today with its low-latency, high throughput query capabilities

Join a stream with a lookup table to enrich the content and produce a materialized view. Then use Debezium to synchronise the lookup table…

Leveraging stateful stream processing to maintain materialized views that are incrementally updated

Learn the fundamentals of materialized views, how they reduce the cost of read queries, and what options they offer to synchronise with…