Designed around your real business problem.
When data systems grow, small engineering issues become operational problems. I help identify bottlenecks and establish practical controls that make data platforms more reliable and efficient.
What this service covers
- ✓Pipeline performance and bottleneck analysis
- ✓Data quality rules and validation checks
- ✓Duplicate detection and reconciliation
- ✓Incremental processing and backfill strategies
- ✓Monitoring, observability and operational controls
- ✓Spark and SQL optimization
- ✓Cloud cost and resource-efficiency reviews
Typical outcomes
- ✓More predictable pipeline execution
- ✓Earlier detection of data issues
- ✓Lower processing time and unnecessary cloud spend
- ✓Clearer operational ownership and troubleshooting paths
Relevant technologies
PySpark · SQL · Databricks · Azure · ETL · Data Quality · Observability · Performance Optimization