DataStage Interview Questions and Answers for 2025

We could use command-line functions to import/ export the DataStage job 

• dsimport.exe- imports the DataStage components. 
• dsexport.exe- exports the DataStage components. 

Additionally, The XML format serves as the basis for moving jobs/dataflows between the Design Studio and DataStage environments. We could export a DataStage job to an XML file and then import it into the Design Studio to migrate the job from one server to another. The imported job is stored as a subflow in the designer. 

A routine is a set of functions defined by DataStage Manager, which is routed through a transformer stage. 

There are three types of routines: 

• Parallel Routines 
• Mainframe Routines 
• Server Routines 

Routines are stored in the Routines branch of the DataStage repository, where they can be created, viewed, or edited, which could be done through the Routine Dialog Box.

The system variables are the read-only variables that are used in the transformer stage or routine. They supply the system information, and these variables start with ‘@.’ 

Few system variables and their uses – 

A container is a group of stages and links. Containers allow you to simplify and modularize your job design by replacing complex areas of your diagram with single container stages. 

These containers are available for both - parallel and server jobs. 

IBM DataStage tool offers two types of containers. 

Local container. They are created within a job and can only be accessed within that job. Local containers are edited on tabs in the job's graph window. Local containers can be used in server jobs or parallel jobs. Their primary use is to "clean up" the job design. 

Shared containers. They are created separately and stored in repository-like jobs. These can be inserted into your job design.

The DataStage director tool is used for: 

• Scheduling, Running, and Monitoring Jobs 
• Viewing Job Status, Logs, and Schedules 
• Filtering Displayed Events 

The DataStage Director window is split into two panes. 

The Job Categories pane shows the ones on the repository. 

The right pane shows one of his three views: Status View, Schedule View, or Log View. 

DataStage Director has three view options. 

• The Status view displays the status, start time, elapsed time, and other execution information for each job in the selected repository category. 
• The Schedule view displays details about job schedules. 
• The log view shows all events for a particular execution of a job. 

InfoSphere DataStage supports different data types from Java and Hive data types. To complete the operation, the connector needs to map data types depending on the scenario in which the connector will be used. You can configure the Hive Connector stage to automatically add columns to the output link at runtime. 

Hive Connector is a tool used to support partition mode when reading data. There are two ways to do this. 

• modulus partition mode 
• min-max partition mode 

The DataStage tool provides support to connect to the HBase dataset and access their database components and tables in the DataStage. It is required to perform the following tasks: 

1. Reading and writing data from HBase to the DataStage tool 
2. Reading the data in parallel mode. 
3. Using the HBase database as a view table in the tool. 

Don't be surprised if this question pops up as one of the top DataStage technical interview questions in your next interview.

There are two basic types of parallelism. Pipelining and partitioning. The DataStage tool provides these two methods. For example, for a simple parallel job that extracts data from one data source, transforms it in some way, and then writes it to another data source. In any case, this job looks the same on the designer's canvas but can be configured to work in different ways depending upon the parallelism selected. 

Pipeline parallelism 

If you run a sample of his job on a system with 3 or more processors, the phase read he will start on one processor and start filling the pipeline with read data. 

In parallel parallelism, the Datastage tool works like a continuous process where the process of transformation, cleaning and load process keeps working in parallel. It is not required that one process of upstream will stop while the downstream process is working and vice-versa. 

This process helps in minimizing the risk usage, which is for the staging area. Additionally, it also reduces the idle time held on the processors working. 

Partition Parallelism 

Suppose you have the same simple pipelined parallelism job, processing a very large amount of data. 

In partition parallelism, the input data stream is divided into various subsets referred to as partitions which are further processed using the individual processors by the same operation process. 

There are 4 tiers – 

• Client tier - The Infosphere Information Server client tier is used for computer development and full management through client programs and consoles. 
• Services tier - The services layer is used to provide standard services such as metadata and logging, as well as other engine-specific services. This includes application servers, various product modules, and other product services. 
• Engine tier - The engine tier consists of a set of logical components used to run the jobs and other tasks of the product modules. 
• Metadata Repository tier - The metadata repository tier includes the metadata repository, the analytics database, and the computer. Used to share metadata, shared data, and configuration information.

splitsubrec restructure operator splits the input subrecords into a set of top-level output vector fields 

splitvect restructure operator promotes the elements of a fixed-length vector to a set of top-level fields with the same name.

One of the most frequently posed scenario based DataStage interview questions and answers, be ready for this conceptual question.

First we look at the file name and how many Is there a record with wc - l filename is counted 

Then first check if the file name has a header and footer, If unavailable, then use following Unix commands 

when we use wc -l filename, it will give count, suppose for example there are 1000 records 

• Ans1: Head -3 filename.txt >Target1.txt 
• Ans2: Tail -3 filename.txt >Target3.txt 
• Ans3: sed -n '4,997p' filename >Target2.txt

We could use the below command using [head] to display the first line of a file. 

$> head -1 file.txt 

When we specify [head -2] then it will print only first 2 records of the file. 

Otherwise, we could use [sed] command. [Sed] is a very powerful text editor which is used for various text manipulation purposes as asked here. 

$> sed '2,$ d' file.txt

Note that in [sed], switch '$' refers to the last line always. So we could use the below command as it will read data from the last line and give output as expected. 

$> sed –i '$ d' file.txt 

Firstly, we use the following script using the [tail] command to get output. 

$> tail -1 file.txt 

If we use the [sed] command, then we use the following command. Remember, ‘$’ reads data from the last line. 

$> sed -n '$ p' test

In [sed], by using the 'd' switch, we could delete a certain line from the output –. So if we want to delete the first line, the following command we use- 

$> sed '1 d' file.txt 

Here it will just print out all the lines except the first line in the file, and hence if we want to delete the first line from the file itself, we need to perform either of two options. 

Either we can redirect the output of the file to another file and then renaming it back to original file as below: 

$> sed '1 d' file.txt > new_file.txt 
$> mv new_file.txt file.txt 

Or else, we could use [sed] switch '–i', which changes the file in place as per the below command. 

$> sed –i '1 d' file.txt 

If you want to delete line by line from a specific file, you can perform your task in a similar way as above. For example:

$> sed –i '5,7 d' file.txt

For running the job in DataStage using the command line, we use the following command where we replace the specified project Name and job name – 

dsjob -run -jobstatus <projectname> <jobname> 

The only difference between hashed and sequential files is that hashed files store data about the hashing algorithm and hash key values, while sequential files do not have key values to store data. 

Based on this hash key function, searching hash files is faster than sequential files. 

There are two types of hash files: 

• static hash files 
• dynamic hash files

NLS stands for National Language Support. This means that the DataStage tools support and can be used in multiple languages, including multi-byte character languages (Chinese, Japanese, Spanish). You can read and write in any language.

• For cleaning up the DataStage repository, we need to follow the following steps - 
• Firstly, go to the DataStage Manager 
• Open the “Job” in the menu bar 
• Select the option of “Clean up resources”. 
• Next, we need to remove the logs, so we need to visit respective jobs and clean up the log files. 

tagbatch restructure operator transforms the tagged fields into an output record whose schema supports all possible fields of a tag instance. 

Tagswitch is used for the contents of tagged aggregates to be converted to InfoSphere DataStage-compatible records.-compatible records.

A collection library is a set of operators used to collect partitioned data. 

The following collector types are available in the Collection Library. 

• Sort-merge collector 
• Round robin collector 
• Ordered collector

A common yet one of the most important DataStage interview questions for experienced, don't miss this one.

InfoSphere DataStage automatically buffers links for certain stages. Its main purpose is to prevent deadlock conditions (where a stage cannot read its input because a previous stage in the job cannot write to its output). 

Jobs with fork joins can lead to deadlock conditions. Here, the stage has two output connections whose data paths are merged later in the job. A situation can arise where all stages in the flow are waiting for other stages to read or write, so none of them can continue. No error or warning message is issued if a deadlock occurs. Your work will wait forever for input. 

InfoSphere DataStage automatically inserts buffering into job flows containing branch joins that can lead to deadlock conditions. In most cases, you do not need to change the default buffering implemented by InfoSphere DataStage.

The DataStage player is the workhorse process. They are useful for parallel processing and are assigned to operators on each node.

Player: One or more logical groupings of processes used to execute data flow logic. All players are created as a group on the same server as the section leader process.

Job Design : 

To achieve the solution, we design the job as shown in the below image. Firstly, we read the seq file as input, then data is passed through a Sort and then Transformer stage to achieve the output as needed in the file. 

b) Sort Stage Properties

We would require to configure the sort stage where we sort the data based on column “Sq” in descending order. 

c) Transformer Stage Properties

Here, we define 2 stage variables: StageVar, StageVar1 and following derivations - 

• StageVar1 = StageVar 
• DSLink6.No = StageVar1 

d) Configuring Output File

Before capturing the data into a Sequential file, we need to sort the data again in ascending order to get the output as needed.

For designing this process, we will use the Transformer stage to flatten the input data and create multiple output rows for each input row.

In the transformer stage –

Configuring the Loop condition –

@ITERATION <= 3

As each input row has three columns of names, we need to process each input row three times so that it will create three separate output rows.

Set the following loop variable –

LoopVar1 we set by the following:

IF (@ITERATION = 1) THEN inlink.Name1

ELSE IF (@ITERATION = 2) THEN inlink.Name2

ELSE inlink.Name3

Defining the Output link metadata and derivations

Col1 - inlink.col1

Col2 - inlink.col2

Name - LoopVar1

We need to design the following job for achieving the task. Here we are reading sequential file as a input, then data is passing through a Sort and Transformer stage to achieve the asked.

In sort Stage –

We need to sort the data based on column “Char” in ascending order.

In transformer stage –

We define 2 stage variable : StageVar, StageVar1, StageVar2

With following derivations -

StageVar = If StageVar1=DSLink6.Char Then StageVar+1 Else1

StageVar1 = DSLink6.Char

Then, create a new column in output which will hold the value of Occurrence of characters and assigned the StageVar.

Occurrence = StageVar

Then, using the in-line sorting, we sort the data on "Occurrence" column in ascending order and populate the output as expected.

Usually, we use max value in a column of a table, but here asked is to get the max value from seq file.

For designing such job, we firstly read the data from flat file, following it we generate a dummy column in column generator stage, next, doing an aggregate on that and sending the output to seq file.

In the column generator stage, we generate the column DUMMY with the value 'X' for all the rows which we are reading from the input seq file. Additionally, we will set the generator for the Cycle algorithm with value 'X' and map the Salary and DUMMY columns in flow to the output of the Column Generator stage.

At the aggregator stage, we will then aggregate on the DUMMY column and then calculate the MAX of Salary. In the nextstep, we map the Max Salary to Output.

We read the sequential file, then do processing in transformer stage and then write the output to file as asked. The job design would be like –

Input data is read from the sequential file stage: 

Nextly, we pass the input data to transformer stage where we separate the alphabets and numeric using 2 stage variables in transformer stage. 

We need to define the stage variables like below: 

svAlpha: Convert("0123456789","",lnk_alphanumerics.MixData) 

svNumerics: Convert(svAlpha,"",lnk_alphanumerics.MixData) 

Nextly we assign these stage variables to output columns as following. 

svAlpha = Alpha 

svNumerics = Number 

In last stage, we write the data into the output file. 

The JSP 2.0 Expression Language (EL) is a simple language that enables you to access object methods with arguments. To access a method with arguments in a JSP page, you need to specify the name of the object, followed by the method name and the arguments in parentheses.

For example, consider the following object with a format method that takes two arguments:

public class Formatter { 
public String format(String s, int i) { 
return s + ": " + i; 
} 
} 

To access the format method and pass it two arguments in a JSP page, you can use the following expression:

${formatter.format("Number", 42)} 

The expression will return the string "Number: 42", which is the result of the format method.

You can also pass EL expressions as arguments to object methods. For example, consider the following object with a format method that takes a variable number of arguments:

public class Formatter { 
public String format(Object... args) { 
return Arrays.toString(args); 
} 
} 

To access the format method and pass it a variable number of arguments in a JSP page, you can use the following expression:

${formatter.format(1, 2, 3, 4, 5)} 

The expression will return the string "[1, 2, 3, 4, 5]", which is the result of the format method.

To pass EL expressions as arguments to object methods, you can enclose the expressions in brackets:

${formatter.format([1 + 1], [2 + 2], [3 + 3])} 

In this example, the expression will pass the values 2, 4, and 6 as arguments to the format method.

You can also use the fn:length function to pass the length of an array or a collection as an argument to a method:

${formatter.format(numbers.length)} 

In this example, the expression will pass the length of the numbers array as an argument to the format method.

To summarize, the JSP EL makes it easy to access and invoke object methods with arguments in a JSP page. You can use it to pass arguments to methods, as well as to pass EL expressions and the length of arrays and collections as arguments.