Article · Data & AI
Data in motion for event-driven applications
Learn the foundations of a Kafka- and Flink-based streaming architecture for real-time ingestion, processing and analytics.
Streaming is not only about making a pipeline faster. It means designing contracts, processing and observability so events can be consumed with context and predictability. Cloudera’s official page presents Kafka and Flink as components of an architecture for ingesting, processing and analyzing data in motion.
Question for your context
Which operational decision needs a trusted event before it can rely on batch processing?
An idea that can shape the next decision.
Learn the foundations of a Kafka- and Flink-based streaming architecture for real-time ingestion, processing and analytics.
Define what a valid event is and who can consume it.
Measure latency, throughput, lag and processing failures.
Plan retention, replay and schema evolution.
Article · Data & AI
Bring the topic into your context.
An initial conversation helps connect the concept to your environment’s priorities, dependencies and capabilities.