Azure Data Engineering Full-Stack Project
Data Engineering / Cloud
A full-stack data engineering project built on Azure, covering the path from source ingestion through processing and warehousing to the analytics layer that consumes it.
- Year
- 2024
- Role
- Data engineer
- Source
- Public repository
Technologies
- Azure
- Python
- Data Pipelines
- ETL
- Cloud
- Analytics
The problem
Cloud data platforms are usually learned in fragments — a pipeline here, a notebook there. What is harder, and more useful, is the joined-up path from raw source to something an analyst can query.
The solution
Build the whole chain on Azure: ingest from source, process and transform, land it in a warehouse layer, and expose it for analytics — so each stage's output is the next stage's contract.
Architecture
How it is put together.
Ingestion
Source data is brought into the cloud storage layer.
Processing
Transformation and cleaning stages prepare the data for modelling.
Warehouse
Processed data lands in a query-ready warehouse layer.
Analytics
The modelled data is exposed for downstream analysis.
Key features
What it actually does.
- Layered ingestion → processing → warehouse → analytics architecture
- Cloud-native pipeline built on Azure services
- Python-based transformation logic
Engineering challenges
The parts that were hard.
Layer boundaries
Keeping transformation logic in its own layer is what stops a cloud pipeline turning into one long, unrunnable script.
Outcomes
What came out of it.
Qualitative outcomes only — no invented benchmarks or metrics.
An end-to-end Azure pipeline from source to analytics
Clear separation between pipeline stages
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Let’s build
Want something like this for your business?
Tell me the problem and I will come back with an architecture, a scope and a timeline.