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SAP BW Archiving for your Back Pocket

Updated: Dec 4, 2023


BW Archiving

Note: All screen captures are for illustration purposes only.


InfoProvider Archiving Modelling


STEP 1 – InfoProviders are fundamental objects in SAP BW where data is stored.


Purpose of Archiving:

It reduces the volume of data in the database, thereby improving query performance and reducing backup times.

It ensures compliance with data retention requirements by moving old data out of the system.


Choose InfoProviders that will be modeled with an archive object the choose Edit.

Call the data archiving process maintenance (transaction RSDAP).

STEP 2 –

The data archiving process contains the same technical name as the InfoProviders. Enter a description.

Choose ADK Based archiving, Nearline or both in the General settings tab.

STEP 3 –

In the Selection profile tab, specify which data from the InfoProvider is selected for the archiving run.

With reference to the selected time characteristic, the time slice archiving always creates time intervals that directly follow on from one another. These can be connected, with conditions, to additional, time-independent partitioning characteristics. You can use these characteristics to restrict the selection further.

To allow for optimum prerequisites for the later use of queries on near-line storage, the selected time characteristic must also be highly relevant for the queries.

For a Datastore object, you can only select a key characteristic as the partitioning characteristic in time slice archiving. The characteristic for the time slice generation can also be a non key characteristic.

However, you must then select a separate partitioning characteristic from the key with a largely monotonous time reference and to which the time conditions can be applied (such as Clearing Date in the data part and Document Number in the key).

At runtime, the system attempts to apply the time restriction to a partitioning characteristic restriction. In the InfoCube, all characteristics always count as key characteristics.

STEP 4 –

You can form semantic groups to archive the data in a sorted way.


The system reads from the database after sorting according to grouping characteristics (the order is important). Records with the same specifications in the grouping characteristics are written to the archive as one data object.


If no characteristic is selected, the storage in the archive is not sorted and technical criteria are used for classification (fixed size) into data objects.

STEP 5 –

Specify the logical file name. Define a maximum size for the generated archive files.

You must take the memory capacity of your storage medium into account. This can be restricted by the number of data objects. The limit that is reached first is the deciding one.


During an archiving run, a new file is created when the maximum size is reached. You can trigger delete jobs by events. You can specify the storage system and delete sequence. You only need to specify the storage system and delete sequence if you are using a separate storage system.

STEP 6 -

Specify a maximum size for the data package. You must take the memory capacity of your storage medium into account. This can be restricted by the number of data objects. The limit that is reached first is the deciding one. During an archiving run, a new data package is created when the maximum size is reached.

Activate the data archiving process. If the data archiving process is active, it is no longer possible to change many of the settings.


InfoCube/DSO Archiving Modelling


DataStore Object (DSO):


1.Definition:

  • DSOs are used for storing detailed, transaction-level data in SAP BW. They can store both transactional and master data.

2. Characteristics:

  • Overwrite Capability: Unlike InfoCubes, DSOs have the ability to overwrite existing records based on unique key fields.

  • Key Fields: Define the uniqueness of a record. New records with the same key as existing ones can overwrite or update the older records.

  • Detailed Data: Provides granularity for deep analysis.

3.Usage:

  • Ideal for data cleansing, consolidation, and storing detailed information. It serves as a foundation layer before data is loaded into other objects like InfoCubes.

InfoCube:


1.Definition:

  • An InfoCube is a multi-dimensional data storage object in SAP BW, designed for analytical reporting. It is structured using facts (measures) and dimensions.

2.Characteristics:

  • Additive Storage: New data in InfoCubes is added and not overwritten, unless explicitly doing so with specific methods.

  • Star Schema: InfoCubes use a star schema where the fact table is at the center surrounded by dimension tables.

3.Aggregated Data: InfoCubes are suited for storing aggregated data, useful for reporting and analysis.


Usage:

  • InfoCubes are optimized for OLAP (Online Analytical Processing) operations, making them perfect for multidimensional analysis.

STEP 1 – Select InfoCube or DSO and Right click to select Change (mode).

STEP 2 –

On the Extras tab and drop down. If this is the first time you are modeling this InfoCube or ODS a prompt asking you if you want to create a new archiving object will be displayed. Respond by choosing on Yes.

STEP 3 –

On the Select Data tab page, choose the selection according to Time Slots method for the Characteristic 0CALDAY Calendar Day.

STEP 4 –

On the File Structure tab page, the Maximum File Size in MByte can be either 50 or 100.

STEP 5 –

On the Folder tab page, the Logical File Name contains the link path to the file directory where the archive files are written, and the file naming convention is implied in this file. The Content Repository id points to a designated area in storage where the archived files will be stored. Select the Store Before Deleting sequence and Delete Program Reads from Storage System. This provides the process checks on the stored file prior to deleting from the online database.

STEP 6 -

On the Delete tab page, select Deletion jobs as not scheduled. This allows the user to select an appropriate time to delete the online data manually.

Have a nice day !

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