Define the purpose
Outline the primary purpose and goals of the data flow to establish a foundation for a design.
Set clear goals for what the flow should achieve.
Include expected outcomes and success criteria.
Align to key business objectives.
Identify the data sources and targets
Identify the source and target systems involved in the flow, where the data will originate and where it will be sent or stored. Understand the connection types, data formats, and access protocols for each, including any characteristics such as varying data loads and structures.
Identify the connections (database, APIs, queues) and access required.
Know the data formats, structures, or schemas involved (e.g., JSON, XML, etc.).
Understand the data characteristics such as volume, velocity, and variety.
Map the data processing logic
Outline the sequence of events needed to process the data for the end goal. Review the appropriate processors for each event, such as ingestion, transformation, enrichment, and routing.
Choose the appropriate processor for each event and step within the flow.
Plan for error handling, null values, and other exceptions.
Minimize unnecessary transformations for flow efficiency.
Identify data dependencies
Determine if any data dependencies exist between the systems or datasets involved within the flow. Understanding these can help ensure data is processed in the correct order and any delays are handled appropriately.
Identify any required sequences or synchronization points.
Ensure data is available when needed by scheduling appropriately.
Plan for failures or delays by using retries or notification.
Plan monitoring and notification
Set up monitoring and notification mechanisms to track performance and health of your flows. The built-in data provenance feature allows you to trace data movement and transformations.
Use data provenance to track data lineage and transformations.
Set up notifications, such as PutEmail, to alert when key events or issues occur,
Design flow control and scheduling
Plan when and how the flow should run by configuring the appropriate scheduling option. Implement flow control logic using processors such as Wait or ControlRate to manage flow timings.
Use Wait to synchronize flows between different branches.
Configure ControlRate to manage data throughput to downstream systems.
Adjust prioritization using attributes, when needed, to process critical records first.
Test and validate
The last crucial step before deploying a flow is to thoroughly test to ensure it performs as expected. Validate each component independently with sample data and conduct end-to-end testing with appropriate datasets.
Test with different data scenarios, including edge cases.
Validate data accuracy at each step.
Simulate expected and peak loads to confirm stability.
Understand performance impacts to external systems.