DataStage is an extract, transform, and load tool that is part of the IBM Infosphere suite. It is a tool that is used for working with large data warehouses and data marts for creating and maintaining a data repository.
We can develop a SQL query or we can use a row generator extract tool through which we can fill the source file in DataStage.
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In DataStage, merging is done when two or more tables are expected to be combined based on their primary key column.
Both these files are serving different purposes in DataStage. A descriptor file contains all the information or description, while a data file is the one that just contains data.
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DataStage and Informatica are both powerful ETL tools, but there are a few differences between the two. DataStage has parallelism and partition concepts for node configuration; whereas in Informatica, there is no support for parallelism in node configuration. Also, DataStage is simpler to use as compared to Informatica.
DataStage Manager defines a collection of functions within a routine. There are basically three types of routines in DataStage, namely, job control routine, before/after subroutine, and transform function.
Duplicates in DataStage can be removed using the sort function. While running the sort function, we need to specify the option which allows for duplicates by setting it to false.
The fundamental difference between these three stages is the amount of memory they take. Other than that how they treat the input requirement and the various records are also factors that differentiate one another. Based on the memory usage, the lookup stage uses a very less amount of memory. Both lookup and merge stages use a huge amount of memory.
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The quality state is used for cleansing the data with the DataStage tool. It is a client-server software tool that is provided as part of the IBM Information Server.
This tool is used for controlling a job or executing multiple jobs in a parallel manner. It is deployed using the Job Control Language within the IBM DataStage tool.
First, we have to select the right configuration files. Then, we need to select the right partition and buffer memory. We have to deal with the sorting of data and handling null-time values. We need to try to use modify, copy, or filter instead of the transformer. Reduce the propagation of unnecessary metadata between various stages.
The term ‘repository’ is another name for a data warehouse. It can be centralized or distributed. The repository table is used for answering ad-hoc, historical, analytical, or complex queries.
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In massive parallel processing, many computers are present in the same chassis. While in the symmetric multiprocessing, there are many processors that share the same hardware resources. Massive parallel processing is called ‘shared nothing’ as there is no aspect between various computers. And it is faster than the symmetric multiprocessing.
To kill a DataStage job, we need to first kill the individual processing ID so that this ensures that the DataStage is killed.
The Compiled Process ensures that the important stage parameters are mapped and these are correct such that it creates an executable job. Whereas in the Validated OK, we make sure that the connections are valid.
If we want to do data conversion in DataStage, then we can use the data conversion function. For this to be successfully executed, we need to ensure that the input or the output to and from the operator is the same, and the record schema needs to be compatible with the operator.
Whenever there is an unfamiliar error happening while executing the job sequencer, all the stages after the exception activity are run. So, this makes the exception activity so important in the DataStage.
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There are different types of lookups in DataStage. These include normal, sparse, range, and caseless lookups.
Using the parallel job or a server job depends on the processing need, functionality, time to implement, and cost. The server job usually runs on a single node, it executes on a DataStage Server Engine and handles small volumes of data. The parallel job runs on multiple nodes; it executes on a DataStage Parallel Engine and handles large volumes of data.
If we want to check whether a certain job is part of the sequence, then we need to right-click on the Manager on the job and then choose the Usage Analysis.
For counting the number of rows in a sequential file, we should use the @INROWNUM variable.
The hash file is based on a hash algorithm, and it can be used with a key value. The sequential file, on the other hand, does not have any key-value column. The hash file can be used as a reference for a lookup, while a sequential file cannot be used for a lookup. Due to the presence fo the hash key, the hash file is easier to search than a sequential file.
For cleaning a DataStage repository, we have to go to DataStage Manager > Job in the menu bar > Clean Up Resources.
If we want to further remove the logs, then we need to go to the respective jobs and clean up the log files.
Routines are stored in the Routine branch of the DataStage repository. This is where we can create, view, or edit all the Routines. The Routines in DataStage could be the following: Job Control Routine, Before-after Subroutine, and Transform function.
An Operational DataStage can be considered as a staging area for real-time analysis for user processing; thus it is a temporary repository. Whereas, the data warehouse is used for long-term data storage needs and has the complete data of the entire business.
NLS means National Language Support. This means we can use this IBM DataStage tool in various languages like multi-byte character languages (Chinese or Japanese). We can read and write in any language and process it as per the requirement.
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