How to Activate and Capture Data Using the SAP Extractor

A program in SAP ERP, the SAP Extractor is widely used for extracting and customizing data from any standard source. This process is done through an exact structure that can be moved to the SAP BW (Business Warehouse). The Extractor also describes a delta load process or multiple types of full load with the SAP BW even getting remote access to the data transfer abilities of the SAP Extractor.


The process of data extraction is started by the SAP Extractor with help from several application-specific extractors. The data is then hard-coded for the Data Source and sent to the BI Content for BW. The Extractor is designed in a manner to match the structure of the Data Source. SAP data can also be extracted from the source systems and transferred to the SAP Business Warehouse through generic extractors. The reason why the SAP extractor can easily recognize which data should be extracted and from which tables the data is to be read once the Data Source is identified by the generic extractor is because the process is fully automated.

There are three types of data extraction that can be carried out with the SAP Extractor.

The first is Content Extraction which is used for the extraction of BW content, FI, HR, CO, SAP CRM, and LO cockpit. Next is the Customer-Generated Extraction where the SAP Extractor is used for LIS, FI-SL, and CO-PA. Finally, there is Generic Extraction that is based on DB View, Infoset, and Function Modules.

Of these three methods, which form of the SAP Extractor is used by an organization depends upon its own unique and specific operational requirements.  

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

SAP Data Extraction with The SAP Extractor

Image
  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.
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