Client Context
A manufacturer moving SAP operational data into a cloud lakehouse relied on SAP, middleware, ETL orchestration, lakehouse, and Power BI to support orders, inventory, production, procurement, and shipment reporting. The operating model handled multiple plants and regional configurations across Europe and Asia, using SAP master and transactional data. Plant-specific mappings produced inconsistent target structures. Batch windows limited time available for manual checks.
Challenges
- Built reusable SAP source-to-target mappings, totals, and field-level validations.
- Added parallel execution, plant parameterization, and pipeline alerts.
- Automated row-count, field-level, aggregate, and source-to-target checks across each ETL stage.
- Validated transformation rules, mappings, defaults, lookups, slowly changing dimensions, and error handling.
- Created test-data packs for positive, negative, boundary, late-arriving, and duplicate scenarios.
- Integrated pipeline tests, scheduler checks, alerts, and reconciliation reports into CI/CD and orchestration tools.
Solutions Implemented
- Automated identity matching, transformation, freshness, and effective-date tests.
- Created negative packs for malformed, duplicate, and conflicting updates.
- Automated row-count, field-level, aggregate, and source-to-target checks across each ETL stage.
- Validated transformation rules, mappings, defaults, lookups, slowly changing dimensions, and error handling.
- Created test-data packs for positive, negative, boundary, late-arriving, and duplicate scenarios.
- Integrated pipeline tests, scheduler checks, alerts, and reconciliation reports into CI/CD and orchestration tools.
Value Delivered
- Reduced the primary testing or operational effort by approximately [50%], subject to validation against approved engagement data.
- Reduced validation time across plants.
- Improved confidence in transformation logic and scheduled data delivery.
- Enabled faster changes to pipelines and analytics products.
Impact Highlights
- Achieved an estimated [40%] improvement in cycle time, coverage, or processing consistency; replace with the approved client metric.
- Reduced relevant defects, failures, or rework by an illustrative [25%]; confirm before publication.
- Improved manufacturing data parity and reporting confidence.
- Created reusable validation patterns across data domains.
