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1. ETL - An development team has some realtime Hydrator pipelines that use a CDAP Worker. They want to run their ETL pipelines using Spark Streaming because their company is standardizing on Spark.

2. Data enrichment - Every time a purchase is made on an online store, an event with purchase information is pushed to Kafka. The event contains a timestamp, purchase id, customer id, item id, and price. A pipeline developer wants to create a realtime pipeline that reads events from Kafka and joins customer information (email, age, gender, etc) to each event, then writes the events to a CDAP Table.

3. Machine Learning - An email client is set up to push an event to a Kafka topic whenever somebody uses the client to send an email. The client is also set up to push an event to another topic whenever an email is marked as spam. A pipeline developer wants to create a realtime pipeline that reads events from spam topic and trains a spam classification model in realtime using Streaming linear regression (http://spark.apache.org/docs/latest/mllib-linear-methods.html#streaming-linear-regression). The pipeline developer also wants to create another pipeline that reads from the email topic and adds a 'isSpam' field to each record based on the model trained by the other pipeline.
4. Windowing - Stock trade events are being pushed to Kafka. A pipeline developer wants to create a realtime pipeline that examines all trades made in various time windows and looks for trades in each window that were made for a significantly different amount of money than other trades in the window. The pipeline makes an HTTP call to some external system for each such trade event.

User Stories

Design