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- (3.4) A developer should be able to create pipelines that contain aggregations (GROUP BY -> count/sum/unique)
- (3.5) A developer should be able to control some parts of the pipeline running before others. For example, one source -> sink branch running before another source -> sink branch.
- (3.54) A developer should be able to use a Spark ML job as a pipeline stage
- (3.4) A developer should be able to rerun failed pipeline runs without reconfiguring the pipeline
- (3.4) A developer should be able to de-duplicate records in a pipeline
- (3.5) A developer should be able to join multiple branches of a pipeline
- (3.5) A developer should be able to use an Explore action as a pipeline stage
- (3.5) A developer should be able to create pipelines that contain Spark Streaming jobs
- (3.5) A developer should be able to create pipelines that run based on various conditions, including input data availability and Kafka events
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