Event-driven Utopia

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

How to detect, capture, and propagate changes in source databases to target systems in real-time, event-driven manner

A story-based introduction to understanding what Debezium is, how it is made of, and how it works in a real-world scenario

How to speed up and scale-out inter-service queries using a dedicated materialized view database that caches queries.

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

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

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