Data Extraction Service
An Azure extraction and transformation platform orchestrating the whole path — raw ingestion, classification, embedding generation, and self-service Power BI reporting business users could run themselves.
- Path
- Ingest → classify → report
- Consumers
- Business users, self-serve
The problem
Analytics work that stalled in the gap between raw data and a usable answer. Getting from ingestion to a report meant a chain of separate steps, each owned by someone technical, which turned every business question into a ticket routed through the data team. The bottleneck was not capability — it was that nobody outside the team could reach the data without help.
The approach
An Azure-based extraction and transformation platform orchestrating the full path:
- Raw ingestion into the data lake as the single entry point, so there was one place where things arrived.
- Classification of incoming content, so downstream processing knew what it was handling rather than inferring it each time.
- Embedding generation, representing content in a form that supported similarity and retrieval rather than exact match alone.
- ETL orchestration through Azure Data Factory and Synapse, making intake to output one managed pipeline instead of a sequence of manual handoffs.
- Power BI reporting on the far end — the piece that turned the platform from an engineering artifact into something business users could serve themselves from.
The outcome
One orchestrated path from raw intake to reporting, with self-service analytics at the end of it. Questions stopped needing to be routed through the data team to get answered, which is the only version of this that actually reduces load rather than relocating it.