Manual ingestion and transform runs required hands-on execution, monitoring and ad-hoc fixes — slow and error-prone.
Azure Data Factory pipelines with scheduled triggers, Databricks notebooks for transformations, and automated monitoring/alerts.
Saved ~8.5 hrs/week of manual ops, ~€38,740 estimated annual savings, faster and more reliable nightly data delivery.
Technology Stack
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
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Could This Work for Your Business?
Manual processes eating 10+ hours/week
Microsoft 365 (Teams, SharePoint, Excel)
Similar results in 5-20 days
Then yes — let's talk.
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