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.
If you're looking for DataStage Interview Questions & Answers for Experienced or Freshers, you are at the right place. There are a lot of opportunities from many reputed companies in the world. According to research, DataStage has a market share of about 3.9%. So, You still have the opportunity to move ahead in your career in DataStage Development. Mindmajix offers Advanced DataStage Interview Questions 2023 that helps you in cracking your interview & acquire your dream career as DataStage Developer.
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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.
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.
To import the DS jobs, dsimport.exe is used, and to export the DS jobs, dsexport.exe is used.
In DataStage 7.5 many new stages are added for more robustness and smooth performance, such as Procedure Stage, Command Stage, Generate Report, etc.
The truncated data error can be fixed by using ENVIRONMENT VARIABLE ‘ IMPORT_REJECT_STRING_FIELD_OVERRUN’.
Merge means to join two or more tables. The two tables are joined on the basis of Primary key columns in both the tables.
As the name implies, data files contain the data and the descriptor file contains the description/information about the data in the data files.
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.
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.
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.
Duplicates can be removed by using the Sort stage. We can use the option, to allow duplicate = false.
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.
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.
The quality stage is also known as the Integrity stage. It assists in integrating different types of data from various sources.
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.
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.
To kill the job in DataStage, we have to kill the respective processing ID.
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.
We can use the date conversion function for this purpose i.e. Oconv(Iconv(Filedname,”Existing Date Format”),” Another Date Format”).
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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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.
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.
We can convert a server job into a parallel job by using the IPC stage and Link Collector.
In DataStage, the Repository is another name for a data warehouse. It can be centralized as well as distributed.
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.
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.
To find rows in a sequential file, we can use the System variable @INROWNUM.
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.
We can clean the DataStage repository by using the Clean Up Resources functionality in the DataStage Manager.
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.
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.
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.
In DataStage, we can drop the index before loading the data in target by using the Direct Load functionality of SQL Loaded Utility.
Yes. Version 8.5 + supports this feature
We can find bugs in the job sequence by using DataStage Director.
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.
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.
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.
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.
In DataStage, MetaStage is used to save metadata that is helpful for data lineage and data analysis.
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.
DataStage is a tool from ETL (Extract, Transform and Load) and DataStage TX is a tool from EAI (Enterprise Application Integration).
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.
There are three types of views in a DataStage Director i.e. Job View, Log View, and Status View.
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.
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.
DRS stage is faster than the ODBC stage because it uses native databases for connectivity.
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.
The DS Designer is used to design work areas and add various links to them.
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 Content Lead at Mindmajix.com. 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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