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Technical Introduction Document: Setting Up and Configuring a Data Pipeline in AnkaFlow

This document provides a step-by-step guide for setting up and configuring a data pipeline using AnkaFlow. The pipeline framework facilitates seamless data transformations and efficient management of stages such as data sourcing (tap), transformations, and sink operations.

1. Overview of the Pipeline System

A pipeline in AnkaFlow defines the flow of data through various stages. Each stage corresponds to a specific data operation such as:

  • Tap Stage: Sources data from a remote or local system.
  • Transform Stage: Processes or transforms data, typically using SQL queries or other computational logic.
  • Sink Stage: Stores the processed data into a target system (e.g., a database, file system, or cloud storage).

These stages are executed sequentially, with each stage building on the data produced by the previous one.

2. Pipeline Components

  • Flow: The primary object that controls the execution of the pipeline. It defines the order of stages, manages connections, and handles error flow control.

Example Initialization: python flow = Flow( defs=stages, context=flowuct_context, default_connection=conn_config, logger=my_logger )

  • defs: A list of stages (e.g., TapStage, TransformStage, SinkStage).
  • context: The dynamic context used in query templates.
  • default_connection: Connection configuration passed to the underlying systems.

  • logger: Optional logger for logging pipeline activities.

  • Datablock: Represents an executable piece of the pipeline. Each Datablock corresponds to a specific stage and includes the logic to execute that stage, e.g., reading data, transforming it, or storing it.

Example: python datablock = Datablock( conn=db_connection, defs=datablock_def, context=flow_context, default_connection=conn_config )

  • Stage Handler: Each stage (tap, transform, sink) is associated with a handler that defines how the stage should be executed. The handler interacts with the data and performs the necessary operations.

3. Configuration and Setup

To configure a pipeline, you must define the following:

  • Stages: Define the sequence of operations your pipeline will execute. Each stage must specify its type (tap, transform, sink) and the corresponding logic (e.g., queries, data manipulations).
  • Connection Configurations: Each stage typically connects to a data source or target system. Connection details, such as credentials, endpoint URLs, and database configurations, are passed into the stages.

Example: yaml stages: - kind: tap name: source_data connection: kind: BigQuery project_id: "my_project" dataset: "my_dataset" - kind: transform name: process_data query: "SELECT * FROM source_data WHERE condition" - kind: sink name: output_data connection: kind: File file_path: "output/data.csv"

  • Variables: If your pipeline stages reference dynamic values, such as dates or keys, you can define these variables within the context to be injected into your queries at runtime.

4. Executing the Pipeline

Once the pipeline is defined, you can execute it using the Flow class. This will initiate the stages in sequence and handle the transformation of data across all stages.

  • Run Pipeline: Execute the pipeline and get the final data. python flow.run()

  • Access Output Data: Once the pipeline runs, you can access the data produced by the final stage via the df() method. python result_df = flow.df()

5. Error Handling and Flow Control

AnkaFlow provides robust error handling mechanisms, ensuring that errors are managed appropriately during pipeline execution. The FlowControl configuration allows you to define how errors should be handled (e.g., fail or warn).

Example of flow control:

flow_control = FlowControl(on_error="fail")

6. Show Schema for Stages

Each stage may expose a schema that defines the structure of the data. This can be useful to inspect and verify the data format before moving to subsequent stages.

schema = flow.show_schema()

7. Testing and Debugging Pipelines

For reliable pipeline execution, it is important to test and debug each stage. AnkaFlow includes utilities for mock testing, simulating remote connections, and verifying the correctness of SQL queries.

  • Unit Tests: Each stage can be tested in isolation. For example, the TapStageHandler can be tested to ensure that it retrieves the correct data from the source.

8. Conclusion

With these components and configurations, AnkaFlow allows you to define, execute, and manage data pipelines flexibly. It integrates various data sources and sinks, applies transformations, and enables efficient error handling, all while keeping the pipeline definitions clean and reusable.

Next Steps: - Customize your pipeline based on the specific sources, transformations, and sinks relevant to your use case. - Explore advanced features like parallel pipeline execution and nested sub-pipelines for more complex workflows.

This document serves as an introduction to configuring a basic pipeline setup in AnkaFlow. For more advanced configurations and features, refer to the detailed documentation on stages, handlers, and connections.