When developing or debugging a stream processing pipeline with Flink SQL, it’s common to inspect each processing step's output to ensure data is being transformed properly. However, comprehending the resulting data stream's structure, distribution, and characteristics entails executing multiple ad-hoc SQL queries, which can be time-consuming and tedious. Additionally, isolating specific subsets of the stream for analysis or debugging often involves even more queries, adding to the complexity and time required.
Interactive tables for Flink SQL Workspaces allow you to quickly explore and visualize Flink query results directly within the Confluent Cloud UI. Users can efficiently scan, analyze, and profile the output data of each query, streamlining the development and troubleshooting processes when implementing stream processing workloads.

#confluent #apacheflink #flink #streamprocessing #datastreaming