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How to obtain a dataflow template from a dataprep Job?

Posted 2 weeks ago
murali2
Bronze 1
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Reply posted on --/--/---- --:-- AM

The client I work for is going to deprecate Dataprep in June. we currently work everything with Google Cloud Platform.

I need to know how I can obtain the template of those dataprep jobs so that when the file is received in the bucket I can trigger the dataflow corresponding to that dataprep and if I can eliminate dataprep from the solution.

Any help would be much appreciated.

Thank you,

Murali.

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Posted on --/--/---- --:-- AM
ms4446
Staff
Reply posted on --/--/---- --:-- AM

Transitioning to Dataflow involves recreating your data transformation workflows and automating pipeline execution.

1. Export Dataprep Job Templates

  • Document Thoroughly: Capture every detail of your Dataprep workflows through detailed descriptions, screenshots, and sample transformations. This comprehensive documentation will be critical in accurately reconstructing the workflows in Dataflow.
  • Explore Export Options: While Dataprep doesn't support direct exports of job templates, investigate third-party tools or scripts that may facilitate the extraction of configurations and logic.
  • Prioritize Complex Flows: Focus first on the most critical or complex workflows. Successfully migrating these can provide valuable learnings and a strong foundation for subsequent migrations.

2. Transition to Dataflow

  • Develop Apache Beam Skills: Ensure your team is proficient with Apache Beam, as it is essential for Dataflow. Consider engaging experts or accessing training resources if needed.
  • Gradual Migration: Adopt a phased approach, starting with simpler workflows and progressively tackling more complex ones. This strategy helps identify potential issues early in the transition.
  • Testing and Validation: Thoroughly test each Dataflow pipeline against its Dataprep counterpart to ensure functional and performance parity.

3. Automate Pipeline Execution

  • Utilize Cloud Functions or Eventarc: Implement these services to trigger Dataflow pipelines in response to specific events, such as new file arrivals in a GCS bucket.
  • Employ Orchestration Tools: If your workflows are complex and involve multiple dependencies, consider using Cloud Composer for enhanced orchestration and scheduling of Dataflow jobs.

4. Decommission Dataprep

  • Phased Removal: Gradually shift workloads from Dataprep to Dataflow, monitoring for any disruptions or issues. Once the new system is stable, fully decommission Dataprep.
  • Continuous Monitoring and Optimization: Regularly monitor the performance and cost-efficiency of your Dataflow pipelines. Adjust configurations to optimize resource use and manage expenses effectively.

5. Additional Considerations

  • Data Lineage and Governance: Implement data lineage tracking within Dataflow to maintain oversight over data transformations and origins.
  • Robust Error Handling: Develop comprehensive error-handling mechanisms to ensure data integrity and efficient recovery from operational setbacks.
  • Cost Management: Actively manage costs by analyzing Dataflow usage and adjusting settings to optimize spending.
  • Leverage Third-Party Tools: Consider third-party tools that can assist in converting Dataprep flows to Dataflow templates, simplifying the migration process.
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