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•  User stories documented (Albert/Alvin)
•  User stories reviewed (Nitin)
•  Design documented (Albert/Alvin)
•  Design reviewed (Andreas/Terence)
•  Feature merged (Albert/Alvin)
•  Examples and guides (Albert/Alvin)
•  Integration tests (Albert/Alvin) 
•  Documentation for feature (Albert/Alvin)
•  Blog post

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1. (3.4) A developer should be able to create pipelines that contain aggregations (GROUP BY -> count/sum/unique)
2. (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. (3.54) A developer should be able to use a Spark ML job as a pipeline stage
4. (3.4) A developer should be able to rerun failed pipeline runs without reconfiguring the pipeline
5. (3.4) A developer should be able to de-duplicate records in a pipeline
6. (3.5) A developer should be able to join multiple branches of a pipeline
7. (3.5) A developer should be able to use an Explore action as a pipeline stage
8. (3.5) A developer should be able to create pipelines that contain Spark Streaming jobs
9. (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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Note: This would also satisfy user story 5, where a unique can be implemented as a Aggregation plugin, where you group by the fields you want to unique, and ignore the Iterable<> in aggregate and just emit the group key.

Story 2: Control Flow (

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Not Reviewed, WIP)

Option 1: Introduce different types of connections. One for data flow, one for control flow

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Story 3: Spark ML in a pipeline

Add a plugin type "sparkMLsparksink" that is treated like a transform.  But instead of being a stage inside a mapper, it is a program in a workflow.  The application will create a transient dataset to act as the input into the program, or an explicit source can be givensink.  When present, a spark program will be used to read data, transform it, then send all transformed results to the sparksink plugin.

{
  "stages": [
    {
      "name": "customersTable",
      "plugin": {
        "name": "Database",
        "type": "batchsource", ...
      }
    },    
    {
      "name": "categorizer",
      "plugin": {
        "name": "SVM",
        "type": "sparkMLsparksink", ...
      }
    },
    {
      "name": "models",
      "plugin": {
        "name": "Table",
        "type": "batchsink", ...
      }
    },
  ],
  "connections": [
    { "from": "customersTable", "to": "categorizer" },
    { "from": "categorizer", "to": "models" }
  ]
}

Story 6: Join (Not Reviewed, WIP)

Add a join plugin type.  Different implementations could be inner join, left outer join, etc.

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