technology

 An integral and critical component of the data retrieval mechanisms in the source systems of SAP are Extractors that can be used to extract data from SAP. An Extractor can fill the structure of a Data Source with the data from the source system datasets of SAP. Replication is the process that is widely used to enable the Data Source and its relevant properties to be known in the SAP Business Warehouse (BW). To extract data from SAP and transfer it to the input layer of SAP Business Warehouse that is the Persistent Staging Area (PSA), the load process with an Info Package in the scheduler needs to be defined. When the Info Package is executed, the data load process is triggered by a request IDoc to the source system. The process chains should be used for executions.  Process to extract data from SAP There are several application-specific extractors, which are hard-coded for the Data Source delivered with the BI Content of the Business Warehouse. These extractors fill the precise struc

 A data lake is a repository of data in its native format – unstructured, semi-structured, or structured – from where it can be easily accessed. This, though, is the basic definition of a data lake and an advanced one with several cutting-edge features like the SAP data lake can do much more. By deploying modern data lake into their existing IT infrastructure, businesses can reap multiple benefits like lowered costs, increased performance, and seamless access to data at all times. The SAP data lake can be run either on the existing cloud environment or a new HANA Cloud instance. In both cases, the storage resources offered is limitless and users can quickly scale up or down in usage as required by paying only for the volumes used. Other features of the SAP data lake include high security and safety through data encryption, audit logging, and monitoring data access. The architecture of the SAP data lake can be visualized as a pyramid. At the top is data that is critical to an organizat

 Data transformation is at the heart of every consumer-centric business today. Small, medium or large, companies are harnessing the power of raw data to generate useful insights and make better business decisions. But to make the most of the data available at hand, businesses need to rely on best practices and flexible solutions. To enable data transformation at scale, businesses need to move and access data in real-time, and that starts with the bulk loading of files. Compared to traditional data loading which is time and resource consuming, loading data in bulk is speedier and more secure, especially for businesses that have a massive volume of information to store, access and process. Bulk loading enables data operators and administrators to load large volumes of data in a more efficient and streamlined way. By saving time on manually entering and uploading files one at a time, bulk loading helps in parallel data uploading using multiple nodes to speed up the process. Further, uplo