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End-to-End Azure Data Pipeline — Nightly ETL Automation (≈8.5 hrs/week saved)

Scheduled ADF pipelines + monitoring that ingest CSVs, transform them in Databricks, and load to Synapse — replacing manual ETL runs and monitoring.

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Problem

Manual ingestion and transform runs required hands-on execution, monitoring and ad-hoc fixes — slow and error-prone.

Solution

Azure Data Factory pipelines with scheduled triggers, Databricks notebooks for transformations, and automated monitoring/alerts.

Outcome

Saved ~8.5 hrs/week of manual ops, ~€38,740 estimated annual savings, faster and more reliable nightly data delivery.

Technology Stack

Azure Data Factory
Databricks
Automation
Monitoring

Implementation Approach

Here's how we designed and built this solution:

  • ADF pipelines with Lookup / ForEach / Copy activities to ingest files into a Bronze ADLS Gen2 layer.
  • Databricks notebooks (silver layer) for cleaning and converting to Parquet formats.
  • Synapse dedicated SQL pool for gold-layer views; nightly triggers and monitoring alerts for failures.
  • Automated logging and basic retry logic to reduce manual retries.

Solution Showcase

📸 Solution View
End-to-End Azure Data Pipeline — Nightly ETL Automation (≈8.5 hrs/week saved) - Main solution screenshot

💡 Want to see this in action? Book a demo to explore the full solution.

Could This Work for Your Business?

If you have:

Manual processes eating 10+ hours/week

If you use:

Microsoft 365 (Teams, SharePoint, Excel)

If you want:

Similar results in 5-20 days

Then yes — let's talk.

Book Free 30-Min Audit

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