DP-600 · 2.2.2 · Prepare data
Enrich data
Enrich data by adding new columns or tables
Add derived values or reference context with SQL, PySpark, Power Query, or joins, depending on whether the result must be stored.
The core idea
Enrichment means either calculating a new value or bringing in context from another table.
Use a view when the derived value should always be current without storage. Use when the enriched result should be materialized.
In , use DataFrame transformations such as withColumn and join. In , use custom columns and merge operations for the same concepts through Power Query.
Key points
- View: a current value without storing it
- : store an enriched result as a new table
- : use withColumn and join in PySpark
- Power Query: add custom columns or merge reference data
Click an underlined term for a one-line explanation, or see them all in the glossary .
Exam trap
A derived value in a view is recalculated at query time. stores a snapshot that must be refreshed or rebuilt.
Sources · 4 Microsoft Learn pages Show ▾
- T-SQL surface area in Fabric Data Warehouse (Microsoft Fabric)
- Tables in Fabric Data Warehouse (Microsoft Fabric)
- Lakehouse tutorial - Prepare and transform lakehouse data (Microsoft Fabric)
- Add a custom column (Power Query)
Checked on October 4, 2026. Report a problem
Go deeper on Microsoft Learn
- T-SQL surface area in Fabric Data Warehouse (Microsoft Fabric)
- Tables in Fabric Data Warehouse (Microsoft Fabric)
- Lakehouse tutorial - Prepare and transform lakehouse data (Microsoft Fabric)
- Add a custom column (Power Query)
Checked against Microsoft Learn on October 4, 2026. Report a problem with this card