Introduction

This section provides information about how to enable InfoProviders to use the NLS by creating a data archiving process (DAP) and how to perform basic operations in addition. (Archive data to NLS and reloading data from NLS to the online database).

Datavard OutBoard is built on two key parts therefore this documentation is also split the same:

OutBoard Kernel – SAP NLS interface and all technical functionality, which is required for storing and archiving BW data, as a technology core of our NLS solution.
OutBoard Cockpit – user interface, which helps to analyze the data volume distribution in the system and also provides functions for managing OutBoarded data to verify volumes and compression ratios.

_{OutBoard for Analytics}

OutBoard for Analytics Kernel

Since the release NetWeaver 2004, SAP provides a NLS interface in form of an ABAP class with several methods that realize the necessary functionality around an NLS scenario.
The OutBoard Kernel is an implementation of the NLS interface. This way, the solution is fully integrated and usable from standard SAP BW interface and transactions. It provides following functionality:
1. Creation of a Data Archiving Process (DAP)
2. OutBoarding of data ("Request-based archiving")
3. InBoarding of data (Reloading of data from the NLS into the online database)
4. Reporting on OutBoarded data
5. Loading data via DTPs ("Data Transfer Processes")
The OutBoard Kernel is completely ABAP-based, which makes it implicitly OS and Database system independent. The main difference from any other NLS implementation is that the data is not stored on an external system, but kept highly compressed (1:20) in the same.
For each archived InfoProvider:
⇒ NLS table is created - where the compressed data gets loaded in(during the write phase)
⇒ When archived data is requested (e.g. Query execution) - it is decompressed and delivered in the desired output format (i.e. by filtering and aggregation).
OutBoard Kernel functions provide privileged database services for compressed storage area tables in the SAP system's database within. It also supports several techniques of optimization (such as indexing and parallelization) to compete with the access time of uncompressed system tables.
Thanks to these features, OutBoard's performance on compressed tables often exceeds the standard performance of online database access.

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