Operate the analytics solution
- Security and governance: access at workspace and item level as well as row, column, object and file level; sensitivity labels, endorsing items
- Development lifecycle: workspace version control, Power BI Desktop projects (.pbip), deployment pipelines
- Impact analysis of downstream dependencies of lakehouses, warehouses, dataflows and semantic models
- Deploy semantic models via the XMLA endpoint; maintain reusable templates (.pbit), data source files (.pbids) and shared models
Prepare data
- Establish data connections, find data via the OneLake catalog and the Real-Time hub, choose the right data store
- Transform: views, functions and stored procedures; star schema for a lakehouse or warehouse; denormalise, aggregate, merge
- Data quality: detect and handle duplicates, missing values and nulls, convert data types, filter
- Query and analyse with the visual query editor, SQL, KQL and DAX
Semantic models
- Choose the storage mode, implement a star schema, relationships including bridge tables and many-to-many relationships
- DAX in depth: variables, iterators, table filters, window functions, information functions
- Calculation groups, dynamic format strings, field parameters, composite models
- Optimisation for large models: query and visual performance, DAX performance, Direct Lake including fallback and refresh behaviour, incremental refresh
