Flow

An Overview

Flows are the backbone of data processing in Ascend, serving as the orchestrators of data movement and transformation within the platform. They allow users to define a sequence of data processing steps, including Read Components, Transforms, Write Components, and Tests, executed on specific data planes like Snowflake, Databricks, or BigQuery.

Key Features of Flows

1. Structured Data Processing: Flows provide a structured approach to data engineering, enabling clear definitions of how data is ingested, transformed, and outputted.
2. Components Integration: Incorporating various components such as Read Components, Transforms, and Write Components, Flows facilitate a modular and efficient data pipeline construction.
3. Scalability and Flexibility: Designed for scalability, Flows can handle data processing across different volumes, velocities, and varieties, accommodating diverse data engineering needs.
4. Monitoring and Management: Through the integration with Ascend's monitoring tools, Flows enable users to track performance, manage resources, and troubleshoot issues within their data pipelines.

Understanding the Structure of Flows

A Flow in Ascend is defined by its YAML configuration, detailing the components involved and their interactions. This configuration outlines the data sources, the transformations to be applied, and the destinations for the processed data. It also specifies operational settings such as data planes, parameters for execution, and resources allocation.

• Data Plane Configuration: Determines where the data resides and where most processing occurs, aligning with the chosen cloud storage and computation platforms.
• Component Specification: Each Flow includes definitions for the sequence of operations on the data, linking Read Components, Transforms, and Write Components.
• Parameterization and Bootstrapping: Flows allow for parameterization, making pipelines adaptable and reusable across different contexts. The bootstrap scripts facilitate custom setup actions before the flow runs.

Best Practices for Designing Flows

• Modular Design: Construct Flows with modularity in mind, allowing for easier updates, testing, and reuse of components.
• Efficiency Optimization: Leverage Ascend's optimization features like incremental processing and partitioning to ensure Flows run efficiently at scale.
• Thorough Testing: Integrate comprehensive testing within Flows to guarantee data quality and integrity throughout the pipeline.
• Monitoring and Maintenance: Regularly monitor Flow performance and resource utilization, making adjustments as needed to maintain optimal pipeline operations.

Conclusion

Flows are the strategic core of data pipeline orchestration in Ascend, enabling users to define and execute complex data processing tasks with precision and efficiency. By understanding the structure, capabilities, and best practices for designing Flows, users can effectively leverage Ascend to streamline their data engineering efforts, from ingestion through transformation to the delivery of insights.