Your data engineering learning path
Follow the sequence, practise each layer and connect the skills through projects.
Foundation
- SQL: querying, T-SQL, modelling, warehousing concepts, performance and interview scenarios.
- Python: programming fundamentals, files, functions, exceptions and data-processing automation.
- PySpark: distributed transformations, joins, optimisation and large-scale data processing.
Azure data platform
- ADLS Gen2: secure, scalable storage and data-lake organisation.
- Azure Data Factory: orchestration, parameters, triggers, incremental loads and monitoring.
- Azure Databricks: notebooks, Delta Lake, medallion architecture and production patterns.
- Azure Synapse Analytics: integrated analytics, SQL pools and enterprise warehousing.
- Event Hubs: real-time ingestion and streaming architecture.
Analytics and unified data platforms
- Power BI: modelling, DAX, Power Query, visualisation and enterprise reporting.
- Microsoft Fabric: Lakehouse, Warehouse, pipelines, notebooks, semantic models and end-to-end analytics.
How to use this path
Study the lessons, practise the demonstrations, complete the exercises, build the project and finish with scenario-based interview preparation.