Best Practices for Handling Bad Data in Stream Processing Platforms

The useful part

Streaming frameworks like Apache Kafka, Apache Flink, Apache Spark Structured Streaming, and stream databases are empowering business folks to process millions of events in real time. What your streaming platform is worth depends exclusively on the quality of data fed into it. An event that is malformed, a duplicate message, any missing field, or an invalid timestamp can lead to incorrect analytics generation, false alert triggers, bursts of alerts, and even application crashes.

How it works

• Batch processing allows for data to be cleaned before execution, but stream-processing requires that validation and corrections occur while the data is flowing.
• Thus, establishing a strong data quality strategy is a core necessity of any event-driven architecture.