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/LSA330/ LSA Scalability Data Flow Split Using a Pass Thru DataStore object

Background

With the new Data Flow Template feature it is now possible describing LSA standards directly in BW.

With BW 7.30 there comes a set of 10 LSA data flow templates as an initial offering.

This blog describes the LSA Data Flow Template LSA330 that illustrates the role of Pass Thru DSOs

For general information about LSA Data Flow Templates please refer to

 

Description

LSA330 is very similar to LSA320.

The Pass Thru DataStore object may serve to bundle an early error handling. Instead of having ‘n’ error DTPs to the ‘n’ partitions of a propagation layer DataStore object, there is just one error DTP from PSA to the Pass Thru DataStore object.

 The Pass Thru DataStore object makes split/semantic partitioning handling easier. Instead of being performed on the PSA element, the split is performed on the Pass Thru DataStore object, which makes it possible to enrich the PSA data for easier DTP selections. (Merge (transform) the records to a common characteristic (e.g. company code to markets) before loading into the Pass Thru DataStore object. It is then possible to transfer the data to the partitioned Propagation Layer DataStore object simply by filtering the DTPs on a few market values. Loading data directly from the PSA element might mean filtering on a significant number of company codes.)

 

Picture 1: LSA330 conceptual view

 

Picture 2: LSA330 – EDW Layers in BW 7.30

 

Picture 3: LSA330 continued – Data Mart Layers in BW 7.30

 

Target Group

Like LSA320, LSA330 addresses dedicated (high volume) data flows that cause load performance issues

LSA330 also addresses the following:

  • The need to investigate loaded records for consistency at an early stage
  • Ease of operation  (as little contact as possible with PSA)
  • Pass Thru DataStore objects  allow making  semantic partitioning of subsequent InfoProviders easier and transparent, if you map the DataSource specific partition criteria & values (like company code, controlling area…) in a transformation rule  to a standard criteria like country. This standard criterion serves as a standard partition and selection criteria on top of the Pass Thru DataStore object for all data flows and InfoProviders. In BW 7.3, the semantically partitioned object in conjunction with a BAdI implementation support creating partitioning templates which can be assigned to multiple partitions. This makes Pass Thru DataStore objects from transparency and ease of implementation perspective obsolete. The other above mentioned aspects remain valid.

 

Implementation Details

Acquisition Layer

Take a look at the Harmonization & Quality Layer.

Note: For more details, see the previous LSA data flow templates 

Harmonization & Quality Layer

  • A Pass Thru DataStore object is of type ‘write optimized’. 
  • The data in a Pass Thru DataStore object should be deleted regularly if a corresponding CM DataStore object exists. 
  • From a modeling perspective, the Pass Thru DataStore object replaces the harmonization layer InfoSource.
  • We recommend naming Pass Thru DataStore objects with a dedicated layer qualifier (T for example)

Note: For more details, see the previous LSA data flow templates 

Corporate Memory Layer (CM)

Pass Thru & Corporate Memory:

  • A Pass Thru DataStore object  has to be cleaned up regularly if a CM DataStore object  exists
  • The Pass Thru DataStore object  normally has the same InfoObjects as the CM DataStore object 
  • You might consider using the Pass Thru DataStore object as your CM DataStore object.  As the volume of the Pass Thru will quickly grow, the CM considerations then apply to the Pass Thru DataStore object (see above). The Pass Thru DataStore object then ceases to be a temporary data store!

Note: Making the Pass Thru DataStore object a CM puts the CM DataStore object directly into the data flow to the Data Marts. This makes the architecture less robust.

Note: For more details, see the previous LSA data flow templates 

Propagation Layer

Note: For more details, see the previous LSA data flow templates 

Business Transformation Layer

Note: For more details, see the previous LSA data flow templates

Reporting Layer

Note: For more details, see the previous LSA data flow templates

Virtualization Layer

Note: For more details, see the previous LSA data flow templates

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