Multi-source batch architecture
Sanitized reference scripts describe extraction from multiple SQL Server sources.
Data engineering · ETL / ELT
COGS Data Pipeline & Analytics ETL
The data foundation behind decision-ready reporting.
Architecture / Technical Case Study
BATCH PIPELINE / TECHNICAL CASE STUDY
A different kind of demonstration.
The current sanitized batch scripts are architecture references. Demo mode exits before connecting or executing the pipeline; no interactive pipeline is deployed.
A bounded synthetic batch-runner strategy is documented for a future demonstration. It has not been implemented.
WHAT TO EXPLORE
- Inspect extraction and transformation design.
- Understand validation and consolidation boundaries.
- Return to this case study for the architecture and implementation context.
01 / THE CHALLENGE
Reliable reporting starts upstream.
Cost-of-goods-sold reporting depends on consistent data from multiple operational sources. Extraction, cleaning, validation and reconciliation must happen before the resulting dataset can be trusted for analysis.
02 / THE SOLUTION
A foundation for cost analytics.
This case study presents sanitized reference code for incremental, full-pipeline and related receiving-analysis batch jobs. It connects that architecture with the data-quality and reconciliation practices described in my CV. It is not a runnable synthetic ETL demonstration.
03 / ARCHITECTURE
From inputs to decisions.
- 01Multiple sources
- 02Extraction
- 03Validation
- 04Transformation
- 05Consolidated dataset
- 06Analytics & reporting
04 / WHAT I BUILT
The engineering behind the workflow.
Configurable source connections
Environment-based configuration externalizes connection settings and source definitions.
Incremental and full job references
The report identifies incremental and full ETL/ELT scripts and a related receiving-analysis job.
Safe default execution
Demo mode exits immediately with an explanation before attempting a database connection.
05 / ANALYTICS & BUSINESS LOGIC
Analysis with operational context.
Data-quality context
The CV documents checks for missing classifications, inaccurate unit conversions, cost mismatches and reconciliation exceptions.
Analytics-ready consolidation
The conceptual flow organizes extraction, validation and transformation before consolidated analytics and reporting.
Automated execution context
The reviewed assets are batch-job references. The portfolio does not demonstrate scheduling or successful end-to-end execution.
06 / TECHNICAL CHALLENGES
Be explicit about what runs.
A complete synthetic substitute for the multi-source batch environment is outside the current demo scope. A referenced pack-size update module was unavailable and is represented by a NotImplementedError stub. This prevents a claim of complete pipeline functionality.
08 / DEMO & PRIVACY
Clear boundaries. Responsible sharing.
Architecture reference · No executing demo
Architecture reference only: demo mode exits immediately without database access. The report verifies compilation, safe exit and placeholder configuration builders—not extraction, transformation, output datasets or an end-to-end run.
This portfolio reference contains no production credentials, patient information, or confidential organizational datasets. It does not run a synthetic-data pipeline.