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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.
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.
- Window and Reduce - Customer locations are being fed into Kafka. Each event contains the customer id and their location. A pipeline developer wants to create a pipeline that reads these events and transforms it into records containing the customer id, speed of the customer, distance of the customer from their home, a flag indicating whether or not the customer just left their home, and demographic information about the customer (age, gender, etc).
- 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.
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