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Before moving to CDS View SAP , let us understand what CDS is. It is a short form for Core Data Services, a programming language developed and launched by SAP. Users need not go through the application server to define and consume models on the database with CDS V iew SAP. Rather , CDS View may be used to create data models with OData services that might be moved outside the applications. This is a feature that is unique to CDS View SAP. Types of CDS View SAP There are two types of CDS View SAP. The first is HANA CDS View which is a programming and virtual data model that comes as a standard feature of SAP HANA. Here businesses can have direct access to all the tables in the SAP database. The second is ABAP CDS View, an important technology that is widely used to develop modern applications on SAP systems. It offers improved performance in data modeling when used together with the SAP HANA database. Developers can use ABAP CDS View to create semantically rich data models and pr...

  In today’s data-driven business environment, the Change Data Capture (CDC) feature gives an edge in operational efficiencies to organizations. CDC stops data breaches by creating a firewall for databases and stores values of all changes made to databases without compromising them in any manner. In the past, businesses had made a demand for these features and various databases had tried varied solutions but without much success. These included triggers, timestamps, complex queries, and data auditing. It was only after Microsoft launched their SQL Server CDC tool that users got what they had always asked for.      Microsoft launched SQL Server CDC IN 2005 with “after update”, “after insert”, and “after delete” capabilities. However, DBAs considered it too complex and invasive and hence, it was not well-received. Based on this feedback, Microsoft launched a new version in 2008 that was technologically advanced with several cutting-edge features. These are still ...

A data lake is a data storage repository where data in its native form can be stored, regardless whether it is in an unstructured, semi-structured, or structured format. When required, this data can be easily accessed and processed for business analytics. If you use a highly optimized repository like the SAP data lake you get several benefits such as improved database performance, lower operating costs, and seamless access to data. Benefits of using the SAP data lake The main benefit of the SAP data lake is derived from its structure which is pyramid-shaped. Stored at the top of the pyramid is data that is frequently used and accessed daily for real-time processing and routine operations. The cost of storing this data is very high. At the middle of the pyramid is data that is not used daily but is important enough to be kept in the system and not deleted. The data storage costs are comparatively lower than the top segment. At the bottom of the pyramid, data that is very ra...

  Why do businesses move databases from SAP to Snowflake? There are a few reasons for this move. First, the nature of transactions in SAP often leads to execution delays, especially in cases where there are large database applications to handle. Next, access permissions are not clearly defined, leading to misperceptions amongst database administrators, and possible data breach scenarios. Given these downsides of SAP, it makes sense to move databases from SAP to Snowflake or other similar cloud-based platforms such as Google BigQuery or Azure Synapse. Let us now go through some benefits of moving databases to the cloud on Snowflake. ·         You get all the benefits of the cloud including access to cutting-edge technologies, unlimited data storage, data compression facilities, and automated fully-managed services. ·         SAP data can be stored in Snowflake in its native format, regardless of whether ...

Databricks is a software company founded by the creators of Apache Spark. Its USP is that it can combine data lakes and data warehouses into a Lakehouse architecture. The process involves manually building a data pipeline with a Python script typically through three easy steps.   ·       *   Setting up SQL Server as a source connector ·       *   Setting up Databricks Lakehouse as a destination connector ·       * Deciding which data to move from SQL Server to Databricks and the periodicity Before you can transfer data from SQL Server to Databricks, some basic functions have to be carried out. ·      *    Have a SQL Server account from where to transfer the data ·     *     Have a Databricks Lakehouse account as the target ·       *   An active cloud account Moving Data From SQL Server To Databricks Moving data ...

Operational Data Provisioning (ODP) is the central infrastructure for data extraction and replication from SAP (ABAP) applications to an SAP BW/4HANA Data Warehouse. In this post, we will explore the various facets of SAP ODP and how this technical infrastructure can be used. There are two ways that the technical infrastructure of SAP ODP can be used to support specific scenarios. ·         Operational Analytics for decision making in operative business processes. ·         Data extraction and replication. Operational data provisioning supports extraction and replication scenarios for various target applications and supports delta mechanisms in these scenarios. In the case of a delta procedure, the data from a source is automatically written to a delta queue using an update process or passed to the delta queue using an extractor interface. Apart from SAP BW/4HANA and SAP BW, Operational Data Provisioning p...

The SQL Server Change Data Capture feature was first launched by Microsoft in 2005 and had advanced “after update”, “after insert”, and “after delete” capabilities. However, due to its invasive and complex nature, it did not find much favor with the DBAs. Consequently, another version was launched in 2008 that enabled DBAs to directly capture changes made to historical data. This technology is still in use today. The Functioning of the SQL Server Change Data Capture SQL Server CDC captures insert, update, and delete changes made to the SQL Server. The details are then stored in an easy-to-understand relational format. All information about changes made to a target database like columns and metadata are available in the CDC. These changes are stored in tables that replicate the structure of the columns of the tracked tables at the source. Access to the changed data in tables is strictly controlled.  The ETL (Extract, Transform, and Load) application is the best example of the...