Supply Chain
Data Pipeline
How we replaced a fragile chain of 23 SSIS packages with a modern Azure Data Factory pipeline — slashing ETL from 6 hours to 45 minutes and achieving 99.7% data quality scores.
Client Context
A regional auto-parts manufacturer with 6 distribution centers relied on a sprawling SQL Server Integration Services (SSIS) architecture to move data between their ERP, warehouse management system, CRM, and reporting data warehouse. Over eight years, this had grown into 23 fragile SSIS packages with hard-coded connection strings, no error handling, and zero monitoring.
When a nightly ETL job failed — which happened 3-4 times per month — the operations team didn't know until the next morning, when warehouse managers reported missing inventory data. Each failure took 2-4 hours of DBA time to diagnose and restart, at an estimated cost of $2,200 per incident.
The data warehouse itself had grown to 1.2TB with no partitioning strategy. Full-load refreshes were the only option, taking 6+ hours and blocking the reporting layer during business hours across time zones.
A Data Pipeline Built on Quicksand
The existing data architecture had no concept of incremental loads, change tracking, or data quality validation. Everything was a full extract-transform-load that treated the data warehouse like it was being rebuilt from scratch every night.
📦 23 Fragile Packages
Each SSIS package was built by a different contractor over 8 years. No standardization, no shared connection managers, and hard-coded server names that broke during every infrastructure change.
⏰ 6-Hour ETL Window
Full-load extracts from the ERP ran for 6+ hours, overlapping with business hours in western time zones. Warehouse managers couldn't access accurate inventory data until after 10 AM.
🔇 Silent Failures
No monitoring, no alerting, no logging. The only failure detection was angry warehouse managers calling the help desk. DBA response time averaged 45 minutes just to begin diagnosis.
📊 Query Performance
The 1.2TB data warehouse had no partitioning, no columnstore indexes, and 200+ stored procedures with cursor-based operations. Complex reports timed out after 10 minutes.
Modern Data Architecture
We designed a three-tier data architecture that separates ingestion, transformation, and presentation layers — each independently scalable and monitorable.
01 — Change Data Capture
Enabled CDC on all 34 source tables in the ERP database. Instead of extracting millions of rows every night, the pipeline now processes only the rows that changed since the last run — typically 0.5% of the total volume.
02 — ADF Pipeline Architecture
Consolidated 23 SSIS packages into 4 parameterized ADF pipelines with shared linked services, dynamic dataset references, and metadata-driven orchestration. Each pipeline is self-documenting and version-controlled in Git.
03 — Warehouse Optimization
Implemented sliding-window table partitioning on all fact tables by month. Added clustered columnstore indexes, reducing storage from 1.2TB to 340GB (72% compression) while accelerating analytical queries by 15×.
04 — Observability Layer
Built an Azure Monitor integration with custom KQL dashboards tracking pipeline duration, row counts, data quality scores, and SLA compliance. Automated PagerDuty alerts for any anomaly detected within 30 seconds of occurrence.
CDC-Driven Incremental Loading
The core optimization was replacing full-table extracts with Change Data Capture incremental loads. The following pattern captures only modified rows, validates data quality in-flight, and merges into the warehouse using an efficient MERGE statement.
This pattern processes an average of 12,000 changed rows per run instead of the previous 2.4 million full-table extract. Combined with columnstore compression, the warehouse now refreshes in 45 minutes — well before the first business user logs in.
Measurable Impact
The migration took 10 weeks, including a 2-week parallel-run validation period where both old and new pipelines ran simultaneously to verify data parity.
| Metric | Before | After | Improvement |
|---|---|---|---|
| ETL Duration | 6 hours | 45 minutes | ▲ 87% faster |
| Pipeline Packages | 23 SSIS packages | 4 ADF pipelines | ▲ 83% simplified |
| Monthly Failures | 3-4 incidents | 0 incidents (6 mo.) | ▲ 100% reliability |
| Data Warehouse Size | 1.2 TB | 340 GB | ▲ 72% smaller |
| Report Query Time | 8-10 min (timeouts) | 15-40 seconds | ▲ 15× faster |
| Failure Detection | Next-day discovery | 30-second alerting | ▲ Real-time |
We used to dread Monday mornings because that's when the weekend ETL failures surfaced. Now the data warehouse refreshes in under an hour and we haven't had a single failure in six months. Our warehouse managers have accurate inventory before their first coffee. The ROI paid for the project in the first quarter.
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