Top 30 Essential Datastage Interview Questions | HTML KICK

Essential Datastage Interview Questions: DataStage is an ETL (Extract, Transform, and Load) tool. It’s a graphical notation that’s used to build/construct data integration solutions. It comes in a variety of variations, including SE, EE, and MVS editions. Job availability can be checked in cities such as Bangalore, Pune, Chennai, and Hyderabad.

Top 30 Essential Datastage Interview Questions

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Knowledge of data warehouses, ETL, DataStage configuration, Design, various stages, and modules in DataStage is required for the DataStage position. It’s used to connect various systems and handle large amounts of data. To design jobs, DataStage includes a user-friendly graphical frontend.

We have interview questions that are specifically developed to help job seekers clear employment interviews. DataStage interview questions and answers might help you prepare for job interviews and get a job offer.

1. What is a data stage?

A data stage is a tool for designing, developing, and executing several applications to fill multiple tables in a data warehouse or data mart. It’s a Windows server tool that extracts data from databases and transforms it into data warehouses. It’s become an indispensable component of IBM’s WebSphere Data Integration package.

2. Can you explain how a source file is filled out?

We can populate a source file in a variety of ways, such as writing a SQL query in Oracle, utilizing the row generator extract tool, and so on.

3. What are the names of the command-line functions for importing and exporting DS jobs?

Dsimport.exe is used to import DS jobs, and dsexport.exe is used to export DS jobs.

4. How does Datastage 7.5 vary from Datastage 7.0?

Many new stages, such as Procedure Stage, Command Stage, Generate Report, and others, have been added to Datastage 7.5 for increased robustness and smooth performance.

5. What are the Functions and Types of Datastage Funnel Stages?

The Funnel stage is a stage in processing. Multiple input data sets are copied to a single output data set. This method can be used to combine multiple data sets into a single large one. There can be an unlimited number of input links and only one output link on the stage.

There are three ways to use the Funnel stage:

The records of the input data are combined in no particular sequence using Continuous Funnel. It takes one record at a time from each input link. If data isn’t accessible on one of the input links, the stage moves on to the next one instead of waiting.

Sort Funnel combines input data in the order indicated by the value(s) of one or more key columns, and these sorting keys determine the order of the output records.

All records from the first input data set are copied to the output data set, followed by all records from the second input data set, and so on.

All input data sets’ metadata must be identical for all methods. Column names should be consistent across all input links.

6. CAN YOU TELL THE DIFFERENCE BETWEEN THE DATA STAGE AND Informatica?

There is a sense of isolation and parallelism in Datastage for node configuration. In Informatica, however, there is no sense of separation or parallelism when it comes to node configuration. In addition, Informatica outperforms Datastage in terms of scalability. When compared to Informatica, Datastage is more user-friendly.

7. Can you tell the difference between a data file and a descriptor file?

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

8. What are the different sorts of routines?

Routines are a collection of functions that the DS manager defines. It can be accessed through the transformer stage. Parallel routines, mainframe routines, and server routines are the three types of routines.

9. Without using the remove duplicate step, what is the best way to get rid of duplicates?

The Sort step can be used to remove duplicates. Allow duplicate = false is an option we can utilize.

10. In datastage PX, how do you construct parallel routines?

Parallel routines can be written in the C or C++ compiler. Routines of this type can also be developed in DS management and invoked from the transformer stage.

11. What is the Quality stage?

The Integrity stage is also known as the Quality stage. It aids in the integration of numerous types of data from a variety of sources.

12. What is the definition of job control?

Job Control Language is the best way to execute job control (JCL). This tool is used to run numerous jobs at the same time without utilizing a loop.

13. How do you tell the difference between symmetric and massive parallel processing?

The hardware resources are shared by the processors in Symmetric Multiprocessing. The CPU runs a single operating system and communicates with one another through shared memory. During Massive Parallel processing, the processor has exclusive access to the hardware resources. Because nothing is shared in this form of processing, it’s also known as Shared Nothing. It outperforms Symmetric Multiprocessing.

14. In the Datastage, what is the difference between validated and compiled?

Validating a job in Datastage is the same as executing a task. During validation, the Datastage engine checks to see if all of the required properties are present. In the other situation, the Datastage engine evaluates whether all of the given properties are legitimate or not while constructing a job.

15. What are the primary distinctions between the stages of Lookup, Join, and Merge?

All are used to join tables; however, there is a distinction.

Lookup is used when the amount of reference data is limited. Because the data is buffered. It will take time to load and find the reference data if it is really huge.

If the reference data is extremely huge, we will use join. Because it reads data from the disc directly. As a result, the processing time will be shorter than the lookup time. However, we are unable to capture the rejected data in join. As a result, we’ve decided to merge.

Merge: We use the merge stage to capture rejected data (when the join key isn’t matched). There is a reject link for each detailed link to capture data that has been rejected.

16. How does Datastage handle date conversion?

For this, we can use Oconv (Iconv (Filename, “Existing Date Format”), “Another Date Format”), which is a data conversion function.

17. In Datastage, why do we employ exception activity?

If an unexpected error occurs while performing the task sequencer, all stages after the exception activity in Datastage are executed.

18. In Datastage, how do you define APT CONFIG?

It is the environment variable that is utilised in Datastage to identify the *.apt file. It’s also where node information, disc storage information, and scratch data are kept.

19. What are the different forms of Datastage Lookups?

Normal lkp and sparse lkp are the two types of Lookups in Datastage. The data is kept in memory first in normal lkp, and then the lookup is performed. The data is directly saved in the database in sparse lkp. As a result, the sparse lkp outperforms the Normal lkp.

20. In Datastage, what are the OConv () and IConv () functions?

The OConv () and IConv() functions in Datastage are used to convert formats from one to another, such as roman numbers, time, date, radix, numerical ASCII, and so on. IConv () is used to convert formats so that the system can understand them. OConv (), on the other hand, is used to convert formats so that consumers can understand them.

21. What are the many kinds of lookups? When is it OK to utilise sparse lookup in a job?

There are two sorts of lookup options available in DS 7.5:

  1. Normal
  2. Sparce

There are three sorts of lookup options available in DS 8.0.1 and later.

  1. Normal
  2. Sparce
  3. Range

Normal lkp: To execute this, lkp data is first stored in memory, and then lkp is conducted, resulting in a longer execution time if the reference data is large. In a normal lookup, the complete table is loaded into memory and the lookup is performed.

Sparse lkp: SQL queries are immediately launched on database related records, resulting in speedier execution than standard lkp. Sparse lookup performs the lookup at the database level.

If the reference link is directly connected to the Db2/OCI Stage, the result is fetched by launching one-by-one queries on the DB table.

Range lookup: this allows you to search for records inside a specific range. Instead of searching the entire recordset, it will only search that specific region of records and deliver good results.

i.e. Select the upper bound and lower bound range columns, as well as the relevant operators, to define the range expression.

Consider the following scenario:

Account Detail.

Trans Date >= Customer Detail.Start Date AND

Account Detail.Trans Date <= Customer Detail.End Date

22. In the Datastage, what’s the difference between validated and compiled?

Validating a job in Datastage is the same as executing a task. The Datastage engine validates whether or not all of the required attributes are present. In the other situation, the Datastage engine verifies whether all of the specified properties are suitable or not while creating a job.

23.  How Do Datstage Implement Complicated Jobs To Recover Performance?

It is recommended that not more than 20 stages be used in each job to restore Datastage performance. If you need to use more than 20 stages, it’s best to save them for the following job.

24. What are some examples of third-party tools that can be used with Datastage?

Autosys, TNG, and Event Co-ordinator are examples of third-party technologies that can be utilised in Datastage.

25. What is the best way to describe a project in Datastage?

We are invited to join a Datastage project whenever we start the Datastage client. Datastage jobs, built-in apparatus, and Datastage Designer or User-Defined components are all included in a Datastage project.

26. What Are Link Partitioner And Link Collector Used For In Datastage?

Link Partitioner in Datastage is used to divide data into different portions using various partitioning methods. Link Collector gathers data from multiple partitions into a single file and saves it in the target table.

27. How Does Datastage Handle Discarded Rows?

Constraints in the transformer handle the discarded rows in the Datastage. We may either save discarded rows in the properties of a transformer or use the REJECTED command to build temporary storage for them.

28. What’s the difference between Datastage and Datastage Tx?

Datastage is an ETL (Extract, Transform, and Load) tool, while Datastage TX is an EAI (Enterprise Application Integration) tool.

This informative blog will teach you everything you need to know about the ETL process.

29. What are the different types of hash files?

In DataStage, there are two sorts of hash files: static hash files and dynamic hash files. When only a little amount of data needs to be put into the destination database, the static hash file is employed. When we don’t know how much data is in the source file, we use the dynamic hash file.

30. In Datastage, what does NLS stand for?

The acronym NLS stands for National Language Support. It can be used to include foreign languages in data, such as French, German, and Spanish, that are required for data warehouse processing. The scripts of these languages are identical to those of English.

Conclusion

You should have a good understanding of Datastage’s design and key features, as well as be able to explain how it differs from other popular ETL solutions.

You should also have a good understanding of the various stages and how to use them, as well as how to create and run a Datastage job from start to finish.

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