Overview
Streaming engineering connects event producers to processing and analytical delivery. The work uses Kafka and Flink alongside Spark-based data processing.
Problem
Continuous workloads require deliberate handling of transformation, delivery and failures. Producing a stream is only one part of a dependable data system.
My role
Work on event ingestion, streaming transformations and integration with AWS data platforms, connecting processed events to downstream data consumers.
Architecture
The conceptual diagram places Kafka between event producers and PySpark / Flink processing. Transformation and enrichment prepare events for storage and analytical consumption.
- 01Event producers
- 02Kafka
- 03PySpark / Flink
- 04Enrichment
- 05Data platform
- 06Analytics
Technical decisions
Keep ingestion, processing and delivery boundaries explicit. Consider fault tolerance and observability with the transformation logic.
Challenges
Streaming requires attention to event continuity, processing failures and downstream delivery.
Data validation
Schema and completeness checks are useful at stream boundaries. Validation should make malformed or incompatible events visible.
- Schema
- Row count
- Partitions
- Null values
- Duplicates
- Data types
- Reconciliation
Performance
Evaluate processing behavior and resource use against the actual event workload.
Outcome
Streaming work links event ingestion, continuous transformation and analytical delivery.
Lessons learned
Treat continuous processing as a full lifecycle: ingestion, transformation, recovery, delivery and monitoring all matter.