Active vs Passive Stages in DataStage.

 

ACTIVE STAGES

Active stages process records and can change the number of rows that go out compared to what went in.

✔ They can:

• Transform data

• Filter rows

• Add or drop rows

• Modify data values

• Perform lookups

• Join datasets

• Group / aggregate data

✔ Have both input and output links

(Some may not require both, but generally yes)

✔ Affect row flow (record count may change)

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Examples of Active Stages

These are common in real jobs:

  Transformer Stage

       Applies business logic row-by-row.

  Aggregator Stage

     Groups and aggregates data.

  Filter Stage

      Filters out rows based on conditions.

  Lookup Stage

    Performs lookups using reference links.

 Join / Merge Stage

    Combines multiple inputs based on keys.

 Sort Stage

   Sorts data (not passive).

Pivot / Unpivot Stage

Changes row/column structure.

Switch / Change Capture / Surrogate Key Generator

Changes shape/flow of data.

Copy Stage

Even though simple, it still processes rows (active).

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PASSIVE STAGES

Passive stages do NOT process or change data.
They simply read or write data.

✔ No business logic

✔ No filtering

✔ No transformation

✔ Row count IN = Row count OUT (except sequential files—header/footer not counted)

These stages only connect DataStage to external systems.

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Examples of Passive Stages

Sequential File Stage

Reads/writes files.

Dataset Stage

Reads/writes DataStage native datasets.

File Set Stage

ODBC / DB2 / Oracle Connectors

Database read/write.

External Source / External Target

FTP / SFTP Stage

Complex Flat File Stage

Peek Stage (only displays data)

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✔ If a stage only reads or writes data → Passive Stage

✔ If a stage transforms, filters, joins, aggregates, or applies logic → Active Stage

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