IBM InfoSphere DataStage



IBM InfoSphere DataStage is an ETL tool and part of the IBM Information Platforms Solutions suite and IBM InfoSphere. It uses a graphical notation to construct data integration solutions and is available in various versions such as the Server Edition and the Enterprise Edition.

A data extraction and transformation program for Windows NT/2000 servers that is used to pull data from legacy databases, flat files and relational databases and convert them into data marts and data warehouses. Formerly a product from Ascential Software Corporation, which IBM acquired in 2005, DataStage became a core component of the IBM WebSphere Data Integration suite.

DataStage originated at VMark[1], a spin off from Prime Computers that developed two notable products: UniVerse database and the DataStage ETL tool.


The first VMark ETL prototype was built by Lee Scheffler in the first half of 1996[1].

Peter Weyman was VMark VP of Strategy and identified the ETL market as an opportunity. He appointed Lee Scheffler as the architect and conceived the product brand name "Stage" to signify modularity and component-orientation[2].

This tag was used to name DataStage and subsequently used in related products QualityStage, ProfileStage, MetaStage and AuditStage.

Lee Scheffler presented the DataStage product overview to the board of VMark in June 1996 and it was approved for development.

The product was in alpha testing in October, beta testing in November and was generally available in January 1997.

VMark acquired UniData in October 1997 and renamed itself to Ardent Software[3]. In 1999 Ardent Software was acquired by Informix[4] the database software vendor.

In April 2001 IBM acquired Informix and took just the database business leaving the data integration tools to be spun off as an independent software company called Ascential Software[5].

In November 2001, Ascential Software Corp. of Westboro, Mass. acquired privately held Torrent Systems Inc. of Cambridge, Mass. for $46 million in cash.

Ascential announced a commitment to integrate Orchestrate's parallel processing capabilities directly into the DataStageXE platform. [6].

In March 2005 IBM acquired Ascential Software[7] and made DataStage part of the WebSphere family as WebSphere DataStage.

In 2006 the product was released as part of the IBM Information Server under the Information Management family but was still known as WebSphere DataStage.

In 2008 the suite was renamed to InfoSphere Information Server and the product was renamed to InfoSphere DataStage[8].

•Enterprise Edition: a name give to the version of DataStage that had a parallel processing architecture and parallel ETL jobs.

•Server Edition: the name of the original version of DataStage representing Server Jobs. Early DataStage versions only contained Server Jobs. DataStage 5 added Sequence Jobs and DataStage 6 added Parallel Jobs via Enterprise Edition.

•MVS Edition: mainframe jobs, developed on a Windows or Unix/Linux platform and transferred to the mainframe as compiled mainframe jobs.

•DataStage for PeopleSoft: a server edition with prebuilt PeopleSoft EPM jobs under an OEM arragement with PeopleSoft and Oracle Corporation.

•DataStage TX: for processing complex transactions and messages, formerly known as Mercator.

•DataStage SOA: Real Time Integration pack can turn server or parallel jobs into SOA services.




Monday, June 30, 2008


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Aggregator Stage

Options Category

Method. The aggregate stage has two modes of operation: hash and sort. Your choice of mode depends primarily on the number of groupings in the input data set, taking into account the amount of memory available. You typically use hash mode for a relatively small number of groups; generally, fewer than about 1000 groups per megabyte of memory to be used.


When using hash mode, you should hash partition the input data set by one or more of the grouping key columns so that all the records in the same group are in the same partition this happens automatically if (auto) is set in the Partitioning tab). However, hash partitioning is not mandatory, you can use any partitioning method you choose if keeping groups together in a single partition is not important. For example, if you’re summing records in each partition and later you’ll add the sums across all partitions, you don’t need all records in a group to be in the same partition to do this. Note, though, that there will be multiple output records for each group.


If the number of groups is large, which can happen if you specify many grouping keys, or if some grouping keys can take on many values, you would normally use sort mode. However, sort mode requires the input data set to have been partition sorted with all of the grouping keys specified as hashing and sorting keys this happens automatically if (auto) is set in the Partitioning tab). Sorting requires a pregrouping operation: after sorting, all records in a given group in the same partition are consecutive.


The method property is set to hash by default.


You may want to try both modes with your particular data and application to determine which gives the better performance. You may find that when calculating statistics on large numbers of groups, sort mode performs better than hash mode, assuming the input data set can be efficiently sorted before it is passed to group.


Allow Null Outputs
. Set this to True to indicate that null is a valid output value when calculating minimum value, maximum value, mean value, standard deviation, standard error, sum, sum of weights, and variance. If False, the null value will have 0 substituted when all input values for the calculation column are null. It is False by default.


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