SAP Data Extraction with The SAP Extractor

 The SAP Extractor (known as the BW Extractor in the past) is typically used for SAP data extraction. The tool extracts data from the source systems and stores it in downstream data warehouses or business intelligence systems.

The SAP Extractor, a data management feature of SAP was launched mainly for SAP data extraction for input into the SAP BW application. Even when an SAP user is not on the SAP Business Warehouse, the Extractor can still be used for data extraction.



For information about SAP data extraction, click here.    

SAP data is used to query Data Services leading to quicker batch processing (previously known as data transformation) based on a pre-defined workflow. Since the Extractor is a program in SAP ERP, it can be triggered for SAP data extraction to transfer to BW.

The options here are either a standard DataSource or a customized DataSource with comprehensive details of full-load delta processes. SAP BW remotely controls the data transfer part of the program.  

For SAP data extraction and transferring it to the input layer of SAP Business Warehouse, the load process with an InfoPackage in the scheduler must be defined. At the time when the InfoPackage is to be implemented with process chains used for executions, the data load process is triggered by a request IDoc to the source system 

Process of SAP Data Extraction  

For SAP data extraction, three ways may be used.

·         Content Extractors: Application-specific, used to extract BW content, FI, HR, CO, SAP CRM, and LO cockpit.

·         Customer-Generated Extractors: Application-specific, used mainly for LIS, FI-SL, and CO-PA.

·         Generic Extractors: Works on the DB View, Infoset, and Function Modules.

The SAP Extractor meets the needs of the SAP Business Warehouse.     

 

 

Comments

Post a Comment

Popular posts from this blog

Streamlining Data Migration: Oracle to Snowflake Transition Explored

Image
In today's data-driven era, seamless data migration between platforms has become a pivotal strategy for organizations looking to optimize their data management and analytics capabilities. One such transition gaining significant attention is the move from Oracle to Snowflake. In this piece, we dive into the intricacies of migrating data from Oracle to Snowflake, outlining the advantages and considerations along the way.   Understanding the Oracle to Snowflake Transition: Transitioning from Oracle to Snowflake involves moving data from Oracle databases, known for their reliability and robustness, to Snowflake's cloud-based data warehousing platform. Snowflake's architecture offers features such as elasticity, scalability, and native support for structured and semi-structured data, making it an appealing destination for data migration.   Key Advantages of Migrating from Oracle to Snowflake:   Scalability and Elasticity: Snowflake's architecture is designed
Read more

The Structure of the SAP Data Lake

Image
In April 2020, SAP launched the SAP HANA Data Lake with the goal of strengthening its existing data storage capabilities and offering customers an affordable storage system. Included were an SAP HANA native storage extension and SAP Data Lake. From the very introduction of this system, several cutting-edge capabilities were added to this package. This led to the SAP Data Lake of the SAP IQ cloud-based system being considered at par with other competitors such as Microsoft Azure and Amazon Simple Storage Service. The structure of the SAP Data Lake is unique and is unlike other data lakes. It resembles a pyramid that is segmented into three parts. The top section holds data that is critical for businesses and is accessed regularly and processed for daily operational requirements. This is vital data often referred to as hot data and the cost of storing it in the SAP Data Lake is the highest. The middle of the pyramid is for storing data that is infrequently used but not as unimportant a
Read more