Introduction to DevOps for Data Products

A Data Product follows a structured lifecycle, progressing through multiple stages such as development, validation, deployment, and maintenance. Managing this lifecycle efficiently requires orchestration and automation to ensure a seamless transition between different phases.

The DevOps module in a data platform provides a framework for managing these transitions by coordinating task execution, monitoring progress, handling errors, and ensuring traceability. Through structured execution pipelines, the module automates manual processes, reducing operational overhead and improving consistency.

Key Concepts

• Stages:
  Stages represent the major phases of a Data Product’s lifecycle. These phases typically include:

  • Development
  • Testing & Validation
  • Deployment
  • Monitoring & Maintenance
    Each stage ensures consistency, traceability, and governance, preventing uncoordinated changes to production environments.

• Activities:
  Activities define higher-level workflows within each stage. They typically group multiple tasks that need to be executed together.
  Examples of activities include:

  • Running a data quality check before deployment.
  • Executing a batch transformation job on an incoming dataset.
  • Validating API responses before publishing an interface.

• Tasks:
  A task is the smallest execution unit in the DevOps workflow. It represents a single automated operation, such as:

  • Triggering a CI/CD pipeline.
  • Running a database migration.
  • Sending a notification upon failure.
    Tasks can be executed sequentially or in parallel, depending on the complexity of the activity.