DataStage Interview Questions

Currently, DataStage is one of the most popular ETL tools on the market. A comprehensive list of DataStage interview questions and answers can be found on this DataStage Interview Questions blog. We've posted a list of frequently asked DataStage interview questions and their comprehensive answers below.

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Top 10 Frequently Asked DataStage Interview Questions

  1. What is DataStage
  2. What is the difference between DataStage 7.5 and 7.0?
  3. Define Merge?
  4. What steps should be taken to improve DataStage jobs?
  5. Define Job control?
  6. Define APT_CONFIG in DataStage?
  7. How to clean the DataStage repository?
  8. Define Meta Stage?
  9. Differentiate between ODBC and DRS stage?
  10. Why do we use the surrogate key?

DataStage Interview Questions and Answers

1. Define DataStage?

A DataStage is basically a tool that is used to design, develop and execute various applications to fill multiple tables in a data warehouse or data marts. It is a program for Windows servers that extracts data from databases and changes them into data warehouses. It has become an essential part of the IBM WebSphere Data Integration Suite.

2. Explain how a source file is populated?

We can populate a source file in many ways such as by creating a SQL query in Oracle, or by using a row generator extract tool, etc.

3. Name the command line functions to import and export the DS jobs?

To import the DS jobs, dsimport.exe is used, and to export the DS jobs, dsexport.exe is used.

4.What is the difference between DataStage 7.5 and 7.0?

In DataStage 7.5 many new stages are added for more robustness and smooth performance, such as Procedure Stage, Command Stage, Generate Report, etc.

5. In DataStage, how you can fix the truncated data error?

The truncated data error can be fixed by using ENVIRONMENT VARIABLE ‘ IMPORT_REJECT_STRING_FIELD_OVERRUN’.

6. Define Merge?

Merge means to join two or more tables. The two tables are joined on the basis of Primary key columns in both the tables.

7. Differentiate between data file and descriptor file?

As the name implies, data files contain the data and the descriptor file contains the description/information about the data in the data files.

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8. Differentiate between DataStage and Informatica?

In DataStage, there is a concept of partition, parallelism for node configuration. While there is no concept of partition and parallelism in Informatica for node configuration. Also, Informatica is more scalable than DataStage. DataStage is more user-friendly as compared to Informatica.

9. Define Routines and their types?

Routines are basically a collection of functions that are defined by the DS manager. It can be called via transformer stage. There are three types of routines such as parallel routines, mainframe routines, and server routines.

10. How can you write parallel routines in DataStage PX?

We can write parallel routines in C or C++ compiler. Such routines are also created in the DS manager and can be called from the transformer stage.

11. What is the method of removing duplicates, without the remove duplicate stage?

Duplicates can be removed by using the Sort stage. We can use the option, to allow duplicate = false.

12. What steps should be taken to improve DataStage jobs?

In order to improve the performance of DataStage jobs, we have to first establish the baselines. Secondly, we should not use only one flow for performance testing. Thirdly, we should work in increments. Then, we should evaluate data skews. Then we should isolate and solve the problems, one by one. After that, we should distribute the file systems to remove bottlenecks, if any. Also, we should not include RDBMS at the start of the testing phase. Last but not the least, we should understand and assess the available tuning knobs.

13. Differentiate between Join, Merge, and Lookup stage?

All the three concepts are different from each other in the way they use the memory storage, compare input requirements, and how they treat various records. Join and Merge needs less memory as compared to the Lookup stage.

14. Explain the Quality stage?

The quality stage is also known as the Integrity stage. It assists in integrating different types of data from various sources.

15. Define Job control?

Job control can be best performed by using Job Control Language (JCL). This tool is used to execute multiple jobs simultaneously, without using any kind of loop.

16. Differentiate between Symmetric Multiprocessing and Massive Parallel Processing?

In Symmetric Multiprocessing, the hardware resources are shared by the processor. The processor has one operating system and it communicates through shared memory. While in Massive Parallel processing, the processor access the hardware resources exclusively. This type of processing is also known as Shared Nothing since nothing is shared in this. It is faster than Symmetric Multiprocessing.

17. What are the steps required to kill the job in DataStage?

To kill the job in DataStage, we have to kill the respective processing ID.

18. Differentiate between validated and Compiled in the DataStage?

In DataStage, validating a job means, executing a job. While validating, the DataStage engine verifies whether all the required properties are provided or not. In another case, while compiling a job, the DataStage engine verifies whether all the given properties are valid or not.

19. How to manage date conversion in DataStage?

We can use the date conversion function for this purpose i.e. Oconv(Iconv(Filedname,”Existing Date Format”),” Another Date Format”).

20. Why do we use exception activity in DataStage?

All the stages after the exception activity in DataStage are executed in case of any unknown error occurs while executing the job sequencer.

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21. Define APT_CONFIG in DataStage?

It is the environment variable that is used to identify the *.apt file in DataStage. It is also used to store the node information, disk storage information, and scratch information.

22. Name the different types of Lookups in DataStage?

There are two types of Lookups in DataStage i.e. Normal lkp and Sparse lkp. In Normal lkp, the data is saved in the memory first and then the lookup is performed. In Sparse lkp, the data is directly saved in the database. Therefore, the Sparse lkp is faster than the Normal lkp.

23. How a server job can be converted to a parallel job?

We can convert a server job into a parallel job by using the IPC stage and Link Collector.

24. Define Repository tables in DataStage?

In DataStage, the Repository is another name for a data warehouse. It can be centralized as well as distributed.

25. Define OConv () and IConv () functions in DataStage?

In DataStage, OConv () and IConv() functions are used to convert formats from one format to another i.e. conversions of roman numbers, time, date, radix, numeral ASCII, etc. IConv () is basically used to convert formats for the system to understand. While, OConv () is used to convert formats for users to understand.

26. Explain Usage Analysis in DataStage?

In DataStage, Usage Analysis is performed within few clicks. Launch DataStage Manager and right-click the job. Then, select Usage Analysis and that’s it.

27. How do you find the number of rows in a sequential file?

To find rows in a sequential file, we can use the System variable @INROWNUM.

28. Differentiate between Hash file and Sequential file?

The only difference between the Hash file and Sequential file is that the Hash file saves data on a hash algorithm and on a hash key value, while the sequential file doesn’t have any key value to save the data. The basis of this hash key feature, searching in a Hash file is faster than in a sequential file.

29. How to clean the DataStage repository?

We can clean the DataStage repository by using the Clean Up Resources functionality in the DataStage Manager.

30. How a routine is called in the DataStage job?

In DataStage, routines are of two types i.e. Before Sub Routines and After Sub Routines. We can call a routine from the transformer stage in DataStage.

31. Differentiate between Operational DataStage (ODS) and Data warehouse?

We can say, ODS is a mini data warehouse. An ODS doesn’t contain information for more than 1 year while a data warehouse contains detailed information regarding the entire business.

32. NLS stands for what in DataStage?

NLS means National Language Support. It can be used to incorporate other languages such as French, German, and Spanish, etc. in the data, required for processing by the data warehouse. These languages have some scripts as the English language.

33. Can you explain how could anyone drop the index before loading the data in target in DataStage?

In DataStage, we can drop the index before loading the data in target by using the Direct Load functionality of SQL Loaded Utility.

34. Does DataStage support slowly changing dimensions?

Yes. Version 8.5 + supports this feature

35. How can one find bugs in the job sequence?

We can find bugs in the job sequence by using DataStage Director.

36. How complex jobs are implemented in DataStage to improve performance?

In order to improve performance in DataStage, it is recommended, not to use more than 20 stages in every job. If you need to use more than 20 stages then it is better to use another job for those stages.

37. Name the third-party tools that can be used in DataStage?

The third-party tools that can be used in DataStage, are Autosys, TNG, and Event Co-ordinator. I have worked with these tools and possess hands-on experience of working with these third-party tools.

38. Define Project in DataStage?

Whenever we launch the DataStage client, we are asked to connect to a DataStage project. A DataStage project contains DataStage jobs, built-in components, and DataStage Designer or User-Defined components.

39. How many types of hash files are there?

There are two types of hash files in DataStage i.e. Static Hash File and Dynamic Hash File. The static hash file is used when a limited amount of data is to be loaded into the target database. The dynamic hash file is used when we don’t know the amount of data from the source file.

40. Define Meta Stage?

In DataStage, MetaStage is used to save metadata that is helpful for data lineage and data analysis.

41. Have you have ever worked in a UNIX environment and why it is useful in DataStage?

Yes, I have worked in the UNIX environment. This knowledge is useful in DataStage because sometimes one has to write UNIX programs such as batch programs to invoke batch processing etc.

42. Differentiate between DataStage and DataStage TX?

DataStage is a tool from ETL (Extract, Transform and Load) and DataStage TX is a tool from EAI (Enterprise Application Integration).

43. What are the size of a transaction and an array mean in a DataStage?

Transaction size means the number of rows written before committing the records in a table. An array size means the number of rows written/read to or from the table respectively.

44. How many types of views are there in a DataStage Director?

There are three types of views in a DataStage Director i.e. Job View, Log View, and Status View.

45. Why do we use the surrogate key?

In DataStage, we use a Surrogate Key instead of a unique key. The surrogate key is mostly used for retrieving data faster. It uses Index to perform the retrieval operation.

46. How rejected rows are managed in DataStage?

In the DataStage, the rejected rows are managed through constraints in the transformer. We can either place the rejected rows in the properties of a transformer or we can create temporary storage for rejected rows with the help of REJECTED command.

47. Differentiate between ODBC and DRS stage?

DRS stage is faster than the ODBC stage because it uses native databases for connectivity.

48. Define Orabulk and BCP stages?

The Orabulk stage is used to load a large amount of data in one target table of the Oracle database. The BCP stage is used to load a large amount of data in one target table of Microsoft SQL Server.

49. Define DS Designer?

The DS Designer is used to design work areas and add various links to them.

50. Why do we use Link Partitioner and Link Collector in DataStage?

In DataStage, Link Partitioner is used to divide data into different parts through certain partitioning methods. Link Collector is used to gather data from various partitions/segments to a single data and save it in the target table.

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Ravindra Savaram is a Technical Lead at His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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