Data Work

In today's fast-paced business environment, it's crucial to have access to accurate and up-to-date information. This is where SAP comes in - a popular enterprise resource planning (ERP) software used by organizations around the world. SAP stores a wealth of data, including financial information, sales and customer data, and supply chain data. This data can be extremely valuable for organizations, but it's often difficult to extract it in a meaningful and usable format. That's where the process of SAP data extraction comes in. SAP data extraction refers to the process of extracting data from SAP systems and transforming it into a format that can be easily analyzed and used for business purposes. With SAP data extraction, organizations can extract the data they need from SAP systems and use it to make informed decisions, improve processes, and increase efficiency. There are several methods for extracting SAP data, including direct database access, application programmi

Data like BigQuery can now be integrated with the business data in the SAP Data Warehouse of the SAP Business Technology Platform using hyper-scaler storage. Data can be directly queried with specific tools through virtual tables. The advantage here is that live connectivity can be established along with the option to query Google BigQuery data from the SAP Data Warehouse Cloud. Hence, businesses can get real-time business analytics for generating critical reports. Further, by replicating data from SAP to BigQuery it is also possible to analyze all third-party and SAP data at a single point and get real-time analytics without having to replicate additional data or ensure live connectivity for querying BigQuery through virtual tables.   For data movement from SAP to BigQuery, the database has to be operating on SAP HANA or any other database that is supported by SAP. The SAP systems data can be combined with that existing in BigQuery by replicating the data from SAP to BigQuery.

Postgres or PostgreSQL as it is known is a widely-used open-source relational database with a wide range of use cases such as data warehouse and analytics to carry out OLTP workloads. Change Data Capture or CDC is software that monitors changes made to source databases to enable performing certain actions based on those changes. Now, taken together, there are many benefits of the Postgres CDC. First, by keeping data warehouses and other downstream systems in sync with PostgreSQL, change events can be captured in real-time. Next, CDC reduces the load on PostgreSQL as only the changes made to the data are processed. Finally, Postgres CDC helps in the seamless implementation of use cases that require access to changes made in PostgreSQL without modifying the application code.  Types of Postgres CDC Trigger-based Postgres CDC: Changes such as Insert, Delete, and Update taking place in the table of interest can be identified. For each change, a row is inserted into a changing table, ther

For large data-driven organizations as well as smaller ones running databases on Microsoft SQL Server, it is beneficial to migrate to the cloud to take benefit of what this environment brings to the table. The migration from SQL Server to S3 ( Amazon Simple Storage Service) can be done without downtime or shutting down the source database. This is possible by using the SQL Server backup and restore method along with the cloud-based Amazon Web Service Database Migration Service (AWS DMS). With this tool, users can migrate database code objects and store procedures.   For more information on SQL Server to S3, click here. This form of SQL Server to S3 database migration is good for SQL Server databases regardless of their size. Data validation time is also saved during the process as validating the target database can be done when data is being replicated from the source to the target. However, there is a limitation here and that is there should be no restrictions for using the SQ

ETL is an acronym for Extract, Transform, Load and is an optimized tool to extract data from a source database, transform it to the data structure that is supported by the intended database, and finally load the processed data into the target database. The SAP ETL tool moves data between various SAP ecosystems and integrates different data formats into each other. An important feature of SAP ETL is that it can even extract data that is not present within an application. There are several cutting-edge features of the SAP ETL tool. ·         It can automatically extract data from the OData services when linked to the CDS views or Data Extractors, both for the first run and incremental data or deltas. ·         Where permission is granted to access the primary database, SAP ETL carries out log-based Change Data Capture activity through transaction logs. ·         SAP ETL automatically merges deltas with the base data and this leads to the tool is continually updated in real-time on the