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DP-750 · 3.3.5 · Prepare and process data

Joins and set operations

Transform data by using join, union, intersect, and except operators

Use joins to combine columns by keys and set operations to combine or compare compatible row sets.

See joins in a real SQL workflow

Azure Databricks SQL editor showing a query, parameters, workspace navigation, and tabular results.

The official editor view shows a JOIN next to its result set, which is exactly the feedback loop you need when checking keys and row counts. The diagrams below separate join semantics from set operations.

Join or combine row sets

  • Join

    Combines
    Related rows by key
    Usually changes
    Columns
    Example
    Orders plus customers

  • Union

    Combines
    Compatible row sets
    Usually changes
    Row count
    Example
    Monthly extracts
Join versus union

A join matches related records. A union appends compatible rows and does not match business keys.

Know the set operations

  • Keep rows from both inputsUNION
  • Keep rows present in bothINTERSECT
  • Keep left rows absent from rightEXCEPT
Row-set operation

Align compatible columns and data types before set operations.

Validate join cardinality

A one-to-many or many-to-many join can multiply rows. Understand key uniqueness before joining so the output grain stays intentional.

Key points

  • Join: Combines related rows using a condition or key
  • Union: Appends compatible row sets
  • Intersect: Keeps rows present in both inputs
  • Except: Keeps rows from the left input absent from the right

Exam trap

A union combines rows; it does not match related records by key.

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