Top DataStage Interview Questions And Answers
Top Answers to DataStage Interview Questions
|Support for Big Data Hadoop||Access Big Data on a distributed file system, JSON support & JDBC integrator|
|Ease of use||Improve speed, flexibility, & efficacy for data integration|
|Deployment||On-premise or cloud as the need dictates|
A data stage is simply a tool which is used to design, develop and execute many applications to fill various tables in data warehouse or data marts.
Learn more about DataStage in this insightful blog post.
We can generate a source file in various ways such as by making a SQL query in Oracle, or by using row generator extract tool etc.
To signify the DS jobs, dsimport.exe is used and to export the DS jobs, dsexport.exe is used.
In Datastage 7.5 various new stages are added for more sturdiness and smooth performance, such as Procedure Stage, Command Stage,etc.
The shortened data error can be fixed by using ENVIRONMENT VARIABLE ‘IMPORT_REJECT_STRING_FIELD_OVERRUN’.
Merge means to merge two or more tables. The two tables are merged on the origin of Primary key columns in both the tables.
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As the name says, data files contains the data and the descriptor file contains the information about the data in the data files.
In datastage, there is a perception of separation, parallelism for node configuration. While, there is no perception of separation and parallelism in informatica for node configuration. Also, Informatica is more scalable than Datastage. Datastage is more easy to use as compared to Informatica.
Routines are basically group of functions that is described by DS manager. It can be called through transformer stage. Routines are of three types such as, parallel routines, server routines and main frame routines.
We can mention parallel routines in C or C++ compiler. Such routines are also developed in DS manager and can be called from transformer stage.
Duplicates can be detached by using Sort stage. We can use the opportunity, as allow duplicate = false.
In order to recover presentation of DataStage jobs, we have to first create the baselines. Secondly, we should not use only one flow for presentation testing. Thirdly, we should work in growth. Then, we should appraise data skews. Then we should separate and solve the problems, one by one. After that, we should allocate the file systems to take away bottlenecks, if any. Also, we should not embrace RDBMS in start of testing phase. Last but not the least, we should understand and evaluate the available tuning knobs.
All the three are dissimilar from each other in the way they use the memory storage, compare input necessities and how they treat various data . Join and Merge needs minimum memory as compared to the Lookup stage.
Quality stage is also called as Integrity stage. It assists in integrating various types of data from different sources.
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Job control can be best performed by using Job Control Language (JCL). This tool is used to execute various jobs concurrently, without using any kind of loop.
In Symmetric Multiprocessing, the hardware resources are communal by processor. The processor has one operating system and it communicates through shared memory. While in Massive Parallel processing, the CPU contact the hardware resources completely. This type of processing is also called as Shared Nothing, as nothing is common in this. It is quicker than the Symmetric Multiprocessing.
To destroy the job in DataStage, we have to kill the individual processing ID.
In Datastage, validating a job means, executing a job. While validating, the DataStage engine checks whether all the necessary properties are given or not. In other case, while compiling a job, the Datastage engine checks that whether all the given property are suitable or not.
We can use date conversion function for this reason i.e. Oconv (Iconv(Filedname,”Existing Date Format”),”Another Date Format”).
All the stages after the exception activity in DataStage are run in case of any unfamiliar error occurs while executing the job sequencer.
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It is the environment variable which is used to recognize the *.apt file in DataStage. It is also used to keep the node information, scratch information and disk storage information.
There are two types of Lookups in DataStage i.e. Normal lookup and Sparse lookup.
We can convert a server job in to a parallel job by using Link Collector and IPC Collector.
In Datastage, the Repository is second name for a data warehouse. It can be federalized as well as circulated.
In Datastage, OConv () and IConv() functions are used to convert formats from one format to another i.e. conversions of time, roman numbers, radix, date, numeral ASCII etc. IConv () is mostly used to change formats for system to understand. While, OConv () is used to change formats for users to understand.
In DataStage, Usage Analysis is done within few clicks. Launch DataStage Manager and right click on job. Then, select Usage Analysis.
To find rows in chronical file, we can use the System variable @INROWNUM.
The only dissimilarity between the Hash file and Sequential file is that the Hash file stores data on hash algorithm and on a hash key value, while sequential file doesn’t have any key value to save the data. Hence we can say that hash key feature, searching in Hash file is faster than in sequential file.
We can clean the DataStage repository via the Clean Up Resources functionality in the DataStage Manager.
We can call a routine from the transformer stage in DataStage job.
We can say, ODS is a small data warehouse. An ODS doesn’t have information for more than 1 year while a data warehouse have detailed information about the entire business.
NLS stand for National Language Support. It can be used to integrate various languages such as French, German, and Spanish etc. in the data, requisite for processing by data warehouse.
In Datastage, we can crash 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 in DataStage.
We can locate bugs in job sequence by using DataStage Director.
In order to recover performance in DataStage, it is suggested, not to use more than 20 stages in every job. If you need to use more than 20 stages then it is advisable to use next job for those stages.
The third party tools that can be used in DataStage, are Autosys, TNG and Event Co-ordinator.
Whenever we begin the DataStage client, we are asked to join to a DataStage project. A Datastage project have DataStage jobs, built-in apparatus and DataStage Designer or User-Defined components.
There are two types of hash files in which are Static Hash File and Dynamic Hash File.
In DataStage, MetaStage is used to store metadata that is beneficial for data lineage and data analysis.
It is useful in DataStage because sometimes one has to write UNIX programs such as batch programs to raise batch processing etc.
DataStage is a tool from ETL i.e. Extract, Transform and Load and DataStage TX is a tool from EAI i.e. Enterprise Application Integration.
Learn more about the ETL process in this insightful blog now.
Transaction size means the number of row written before committing the account 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. Log View, Job View and Status View.
Surrogate key is mostly used for getting data faster. It uses catalog to perform the retrieval operation.
In the DataStage, the discarded rows are managed by constraints in transformer. We can either place the discarded rows in the properties of a transformer or we can create a brief storage for discarded rows with the help of REJECTED command.
DRS stage is faster than the ODBC stage because it uses local databases for connectivity.
Orabulk stage is used to store big amount of data in one target table of Oracle database. The BCP stage is used to store big amount of data in one target table of Microsoft SQL Server.
The DS Designer is used to make work area and add many links to it.
In DataStage, Link Partitioner is used to split data into various parts by certain partitioning methods. Link Collector is used to collect data from many partitions to a single data and save it in the target table.
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