Introduction

DataStage is an ETL-(Extract, Transport, Load) tool in the technologically-enabled market that uses large volumes of different kinds of data. The questions and answers set out below are designed to help one understand the concepts better, revise the fundamentals, and be prepared for a gamut of questions based on DataStage questions which are loved by the interviewers and demonstrate a good understanding of the subject at an Interview.

The DataStage interview questions are divided into various sets meant for freshers and beginners in the subject, those who have a fair understanding of the subject at the intermediate level, and those who have some DataStage developer experience on the subject at the level of advanced questions. So, get ready, set with the answers, and go crack that interview with confidence.

Basic DataStage interview questions:

1. Explain the characteristics of DataStage.

When supporting Big Data Hadoop it permits a distributed file access to Big Data and works with JDBC Integrator while supporting JSON. It improves the data integration flexibility, efficacy, and speed being user friendly and can be a cloud or an on-premise deployment.

2. Explain the IBM DataStage?

In the IBM Infosphere suite, DataStage works in ETL (extract/ transform/ load) modes tool while maintaining and creating a depository of data for warehouses of large data.

3. How is the source file filled in a DataStage?

One can use an extract tool like the row generator or develop the query in SQL to fill in the Data Stage’s source file.

4. Explain DataStage merging operation?

When two/ more tables need their primary key columns to be combined, DataStage does a data merge operation on the tables.

5. What are descriptors and data files?

The files have different DataStage purposes with the descriptor file having descriptions or information and data files having only data.

6. Explain differences in Informatica and DataStage.

Both are ETL tools. DataStage uses concepts of partition and parallel connections for configuring the nodes. Informatica lacks node configuration in parallelism. Compared to Informatica, DataStage is much more user-friendly and easy to use.

7. Explain a DataStage routine.

DataStage’s Manager uses a routine-filled collection of functions that have 3 types of routines. Namely, transform function routine, the before/after sub-routine, and the job-control routine.

8. How does one remove DataStage duplicates?

DataStage removes duplicates via the sort function. In running the sort function to remove duplicates, one needs to set the options of duplicates which permits duplicates to false.

9. Explain the differences between merge, join, and lookup stages.

One of the main differences in the above functions at the three stages of lookup, join and merge are the memory each of these use. Other factors that affect the operations are the way records are handled and the input handling of each of these operations. The lookup stage requires the least memory whereas the join and merge operations need huge memory volumes.

Intermediate DataStage interview questions:

10. What is the DataStage’s quality state?

This tool in IBM’s Information Server is used to clean data of the client-server software using the DataStage tool. 

11. Explain DataStage job control.

The job control tool executes and controls multiple jobs happening in a manner of parallel jobs. IBM’s DataStage tool uses tools of Job Control Language to do this.

12. How to do performance tuning of DataStage jobs?

The process involves configuration files, the right amounts of buffer memory, and partition memory selection. It is followed by data sorting and null-time value handling. Instead of the transform function, one would use functions like copy,  modify, filter, etc and reduce un-required metadata propagation between the many stages.

13. What is a DataStage repository table?

A ‘repository is a data warehouse that may be distributed or centralized and used to answer historical, ad-hoc, complex, and /or analytical queries.

14. Compare symmetric multiprocessing and massively parallel processing.

To use massively parallel processing the chassis has many computers working on it. In symmetric multiprocessing, the hardware has many processors sharing its resources of hardware. Massive parallel processing aka ‘shared nothing’ is faster and has no aspect between the many computers on its chassis. 

15. How does one kill a job in DataStage?

One has to kill the ID with individual processing firstly to kill the job in DataStage.

16. Compare Compiled Process and Validated OK in DataStage.

Validated OK makes sure that all connections are validated whereas the Compiled Process ensures all crucial parameters are correctly mapped to create a job that is executable.

17. Explain features of DataStage’s data type conversion.

While doing data conversion one uses the function of data conversion in DataStage. Important factors in its execution are that the record schema should be operator compatible and the operator’s output and input from and to should be the same.

18. Explain the significance of DataStage’s exception activity?

In DataStage, if the job sequencer execution is affected by any unfamiliar error, all stages post the exception activity are to be run making the exception activity crucial.

Advance DataStage interview questions:

19. Explain the types of lookup stage in DataStage.

DataStage has lookups. that is normal, range, sparse and caseless.

20. Differentiate between the use of a server job and a parallel job.

Depending on the processing need, cost, functionality, and time to implement factors one would choose the server or parallel job. The single-node server job execution in DataStage can handle data volumes that are small. If data volumes are large one would use the multiple noded DataStage to run parallel jobs.

21. Explain DataStage Usage Analysis.

Use Datastage Manager’s job tab and right-click on it and then choose Usage Analysis to check if the sequence contains a particular job.

22. How does one find a sequential file’s number of rows?

Use the @INROWNUM variable to count the sequential file’s number of rows.

23. Explain differences in hash and sequential files.

The hash file is used with a key-value and runs on the hash algorithm. Sequential files do not have the column for key-value. The hash file is oft used as lookup reference while lookups do not use sequential files. The hash key makes it easier to search for hash files when compared to sequential files.

24. How do we clean a DataStage repository?

To clean the repository, use DataStage Manager and choose the job in its menu bar. Click the tab ‘Clean Up Resources’. To remove logs one needs to go to the job and clean up the job’s log files.

25. What is a DataStage call a routine?

The DataStage repository stores routines in its Routine branch and one can view, create and edit the Routines. The Routine types could be Before-after Subroutine, Job Control Routine, and the Transform function.

Conclusion

Here’s hoping this article and the DataStage interview questions helps in the interview preparation. Interviewers also have a bunch of configuration file in DataStage, FileNet interview questions, Sterling Integrator interview questions, DataStage vs Informatica, join stage in DataStage, routines in DataStage, sequential file stage in DataStage, types of lookup in DataStage, interview questions on transformer stage in DataStage, DataStage partitioning interview questions repository of questions. It is recommended one prepares more such DataStage interview questions and answers for the interview. All the best for the job interview!

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