The Functioning of SQL Server Change Data Capture

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 use of this SQLServer Change Data Capture technology. The application moves the change data from the SQL source tables incrementally to a data mart or a data warehouse.  

What attributes make the SQL Server Change Data Capture a leader I    n this field? In other technologies, the source tables in a data warehouse must be constantly refreshed to track any changes made to them. This is a very tedious and time-consuming process. SQL Server CDC, on the other hand, allows a smooth flow of data, helping users to apply it to various target platforms.

This is how SQL Server Change Data Capture works and why it is so important for large data-driven organizations.    

Comments

Post a Comment

Popular posts from this blog

Unleashing the Power of Snowflake Data Lake Integration Streamlining Data Management and Analysis

Image
Snowflake Data Lake integration offers organizations a seamless and efficient solution for managing and analyzing large volumes of data stored in data lakes. By combining the scalability and flexibility of Snowflake's cloud data platform with the robust storage capabilities of data lakes, organizations can unlock new possibilities for data-driven insights, decision-making, and innovation. Let's explore the benefits and capabilities of Snowflake Data Lake integration and how it can revolutionize your data management and analysis strategies. Unified Data Repository Snowflake Data Lake integration enables organizations to create a unified data repository that consolidates data from various sources, including structured and semi-structured data stored in data lakes. By integrating Snowflake's data warehouse capabilities with data lakes, organizations can eliminate data silos and achieve a single source of truth for their data, enabling streamlined data access, analysis, and...
Read more

An Overview of the SAP BW Extractor

Image
A SAP BW Extractor, also known simply as the SAP Extractor is an optimized data management tool of SAP. Its main function is to extract SAP data from a source system and move it to a downstream data warehouse or store it in a business intelligence system. The SAP BW Extractor feeds SAP BW applications with data extracted from SAP. Functioning of the SAP BW Extractor The SAP BW Extractor integrates SAP data even if the SAP user is not on the Business Warehouse (BW). Hence, this tool can seamlessly populate the delta queue with changed records of the new delta type.    Before using a SAP BW Extractor, it must be hard-coded to ensure that the application-specific Extractor delivers BI Content of the Business Warehouse. The functioning of the Extractor is fully automated. It can identify both the data that is extracted and the tables where it is stored in a read-only format after the Extractor is named in the Data Source. Additionally, a SAP BW Extractor has master da...
Read more