Target Audience: Data Engineers, PySpark Developers, and Analytics Engineers
Estimated Read Time: 13 min01PySpark & Notebook Performance Tuning
Optimize Spark notebook execution in Fabric by tuning high-concurrency pools and dynamic allocation. Avoid small-file degradation by scheduling Delta OPTIMIZE and VACUUM maintenance tasks. Utilize V-Order parquet sorting to accelerate downstream Power BI DirectLake and T-SQL Warehouse query execution.
Implementation & Verification Checklist
- Enable V-Order on all Silver and Gold Delta table write operations
- Schedule regular OPTIMIZE and VACUUM maintenance pipelines to manage small files
- Parameterize environment variables, secrets (Azure Key Vault), and connection strings
- Capture structured execution logs, row counts, and data quality check results for every run
02Data Factory Pipelines & Dataflows Gen2
Use Dataflows Gen2 for low-code transformation and business analyst ingest, while reserving Data Factory Orchestration Pipelines for high-throughput batch loads, notebook execution chains, and control-flow branching. Implement idempotent retry logic and dead-letter exception queues for pipeline resiliency.
Implementation & Verification Checklist
- Ensure pipeline copy activities utilize binary/parquet staging for bulk data movement
- Implement exponential back-off retries on transient network and API failures
- Separate control-flow pipelines from worker notebooks to allow parallel execution
- Audit pipeline run durations and setup alert notifications on SLA breaches