IBM InfoSphere DataStage Interview Questions

Set K

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DataStage Interview Questions

Question 01:

What is Modify Stage in DataStage?
Answer:
The Modify Stage is used for high-performance data transformation in parallel jobs. It performs simple operations like column renaming, type conversion, dropping columns, and derivations using a faster internal engine compared to Transformer Stage.

Question 02:

Why is Modify Stage faster than Transformer Stage?
Answer:
Modify Stage uses native parallel processing (C++ based processing) and avoids row-by-row interpretation, making it significantly faster for simple transformations.

Question 03:

What types of operations can Modify Stage perform?
Answer:

Column rename
Data type conversion
Dropping columns
Adding new columns
Simple derivations

Question 04:

Can Modify Stage handle complex logic?
Answer:
No, it is designed only for simple transformations. Complex logic requires Transformer Stage.

Question 05:

What is the syntax used in Modify Stage?
Answer:
It uses a derivation expression language (similar to C-style expressions).

Question 06:

What is a typical use case of Modify Stage?
Answer:

Converting data types (string → integer)
Renaming columns
Removing unnecessary columns

Question 07:

Can Modify Stage be used for filtering?
Answer:
No, filtering is done using Filter Stage.

Question 08:

What is column mapping in Modify Stage?
Answer:
Defining how input columns are mapped to output columns.

Question 09:

What is null handling in Modify Stage?
Answer:
It supports basic null handling using conditional expressions.

Question 10:

When should you prefer Modify over Transformer?
Answer:
When transformations are simple and performance is critical.

Question 11:

Does Modify Stage support constraints?
Answer:
No, it does not support constraints like Transformer.

Question 12:

What is "Drop Column" in Modify?
Answer:
Removes unnecessary columns from the dataset.

Question 13:

Can Modify Stage change column order?
Answer:
Yes.

Question 14:

What is metadata handling in Modify Stage?
Answer:
It modifies metadata along with data transformation.

Question 15:

Limitations of Modify Stage?
Answer:

No complex logic
No constraints
No stage variables

🟣 Filter Stage

Question 16:

What is Filter Stage in DataStage?
Answer:
The Filter Stage is used to filter rows based on conditions and route them to different output links.

Question 17:

How does Filter Stage work?
Answer:
It evaluates conditions and sends records to matching output links.

Question 18:

Can Filter Stage have multiple outputs?
Answer:
Yes.

Question 19:

What happens if no condition matches?
Answer:
Record is discarded or sent to default output.

Question 20:

What type of conditions are used?
Answer:
Boolean expressions (e.g., salary > 10000).

Question 21:

What is default output link?
Answer:
Captures records that do not match any condition.

Question 22:

Can Filter Stage perform transformations?
Answer:
No, only filtering.

Question 23:

Difference between Filter and Transformer?
Answer:

Filter → only filtering
Transformer → filtering + transformation

Question 24:

Performance of Filter Stage?
Answer:
High, since it is lightweight.

Question 25:

When to use Filter Stage?
Answer:
When only filtering logic is required.

Question 26:

Can Filter Stage reject records?
Answer:
Yes.

Question 27:

What is constraint in Filter Stage?
Answer:
Condition applied to filter data.

Question 28:

Can Filter Stage be replaced by Transformer?
Answer:
Yes, but Filter is more efficient.

Question 29:

Does Filter Stage require sorting?
Answer:
No.

Question 30:

Example use case?
Answer:
Separating active and inactive customers.

🟡 Copy Stage

Question 31:

What is Copy Stage?
Answer:
The Copy Stage is used to duplicate input data to multiple output links.

Question 32:

What is purpose of Copy Stage?
Answer:
To send same data to multiple stages.

Question 33:

Does Copy Stage modify data?
Answer:
No.

Question 34:

Can Copy Stage have multiple outputs?
Answer:
Yes.

Question 35:

What is round robin option in Copy Stage?
Answer:
Distributes data evenly across outputs.

Question 36:

What is difference between Copy and Funnel?
Answer:

Copy → 1 input → multiple outputs
Funnel → multiple inputs → 1 output

Question 37:

Is Copy Stage parallel?
Answer:
Yes.

Question 38:

When to use Copy Stage?
Answer:
When same dataset is needed in multiple flows.

Question 39:

Performance of Copy Stage?
Answer:
Very high (minimal processing).

Question 40:

Does Copy Stage require sorting?
Answer:
No.

🔴 Switch Stage

Question 41:

What is Switch Stage?
Answer:
The Switch Stage routes data to different outputs based on a single key column value.

Question 42:

How is Switch different from Filter?
Answer:

Switch → based on single column value
Filter → multiple conditions

Question 43:

What is key column in Switch?
Answer:
Column used for routing data.

Question 44:

Can Switch have multiple outputs?
Answer:
Yes.

Question 45:

What happens if no case matches?
Answer:
Record goes to default link.

Question 46:

Is Switch faster than Filter?
Answer:
Yes, for simple routing.

Question 47:

Example use case of Switch?
Answer:
Routing data based on region or category.

Question 48:

Does Switch support complex conditions?
Answer:
No.

Question 49:

When to use Switch over Filter?
Answer:
When routing is based on one column.

Question 50:

Best practices for Modify & Filter stages?
Answer:

Use Modify for simple transformations
Use Filter for row-level filtering
Use Copy to duplicate data
Use Switch for routing
Avoid using Transformer unnecessarily
Optimize for performance

IBM InfoSphere DataStage Interview Questions - Set K