Introduction

A rules engine transform will apply predefined rules to incoming data (realtime as well as batch). Rules must be generic enough to allow updates to a dataset, posting to an HTTP endpoint, sending an email, etc.

Use case(s)

• Use case #1
• Use case #2
• Use case #3
• Use case #nCompanyA wants to develop a streaming pipeline to read and process signals from wearable and non-wearable devices, and apply rules on incoming signals. Based on the rules, it wants to send notifications to configured mobile devices to provide concierge and/or healthcare services. 
• CompanyA has a rules management system that can allow users to feed in rules for devices. Rules can state actions to be taken if certain conditions are met in the signals from the provided devices. These rules are stored in a CDAP dataset. A streaming pipeline will then read the rules dataset and apply rules applicable for incoming signals to trigger appropriate notifications.

User Storie(s)

• User story #1
• User story #2
• User story #3
• User story #m

Plugin Type

•  Batch Source
•  Batch Sink 
•  Real-time Source
•  Real-time Sink
•  Action
•  Post-Run Action
•  Aggregate
•  Join
•  Spark Model
•  Spark Compute
•  Transform

Configurables

This section defines properties that are configurable for this plugin. 

Design / Implementation Tips

• Tip #1
• Tip #2

Design

Approach(s)

Properties

Security

Limitation(s)

Future Work

• Some future work – HYDRATOR-99999
• Another future work – HYDRATOR-99999

Test Case(s)

• Test case #1
• Test case #2

Sample Pipeline

Please attach one or more sample pipeline(s) and associated data. 

Pipeline #1

Pipeline #2