The Ultimate Guide To SQL Server CDC – Evolution, Functions, And Types

This post will explore the various aspects of Microsoft SQL Server CDC, ranging from its development over the years, its functioning, and the types of SQL Server Change Data Capture. 

Evolution of Microsoft SQL Server Change Data Capture (CDC) 

In the modern digital environment, one of the prime concerns of businesses is to ensure data durability, reliability, and security. It is essential to make sure that all data is firewalled from breaches and hackers so that confidential information is not compromised and their history cannot be tampered with. 

To get an ideal solution to this issue, various resolutions have been tried in the past. These included triggers, timestamps, data auditing, and complex queries. None of them could produce the desired result and the matter remained in limbo till Microsoft SQL Server CDC stepped into the picture.

In 2005, Microsoft launched their SQL Server Change Data Capture (CDC) product that came with “after update”, “after insert”, and “after delete” features. Still in the experiment stage, it did not find favor with database administrators due to its complexities.

Quick to react to the feedback, Microsoft introduced a modified version. It was simpler and DBAs could capture and archive changes and historical data without having to go through any other additional activities. It quickly gained popularity and the format is still in use today.  

The Concept of SQL Server CDC Explained

Through SQL Server CDC, users can make changes such as insert, delete, and update to databases that can be later accessed in an easy-to-read relational format. For the rows where one or more modifications have been done, all inputs that are required to capture the changes made to a target database like column information and meta data are available. 

These changes are then stored in tables that reflect the structure of the columns of the tracked stored tables. Necessary precautions like table-valued functions are used to control access to these changes, thereby ensuring strict data security.

Why is Microsoft SQL Server CDC a cut above the others in this niche? Typically, source tables in a data warehouse need to be continuously refreshed to mirror all changes made to them. Apart from being Highly time-consuming, this procedure can be very tedious too. 

Check this out with the capabilities of SQL Server CDC. This technology ensures a smooth flow of change data that is structured in a way that users can apply it directly to various target platforms as required. 

The Extract, Transform, and Load (ETL) application is one of the best examples of a consumer-oriented technology using SQL Server CDC. Here, change data from SQL source tables are incrementally moved by an ETL application to a data warehouse or a data mart.

Functioning of Microsoft SQL Server CDC 

Change Data Capture tracks and monitors all changes made by users in tables and then stores them in relational tables that can be seamlessly accessed by T-SQL for quick retrieval of data. A mirror image is created of the tracked table whenever Change Data Capture is applied to a database table. 

The structure of the columns of the replicated tables has additional columns of metadata that identify the changes made in the database rows. This is the only point of difference between the source tables and replicated ones with the two being similar in all other respects. Once the SQL Server CDC process is completed, the new audit tables are used to monitor the logged tables and track all activities that have occurred. 

The source of the changes made in CDC is shown in the transaction log of the SQL Server CDC. As soon as any change such as insert, update, or delete is seen in the tracked source tables, the details of these entries are added to the log and become an integral part of CDC. This log that contains details of the changes is then read and the modifications made are linked to the change table part of the original table. 

Types of SQL Server CDC

There are two types of SQL Server CDC.

Log-based Change Data Capture

This a straightforward process where the transaction log and file of a database are analyzed by the system to know about the changes made at the source. Next, all these changes are replicated in the target database. 

The advantage of this form of CDC is that it is very reliable as all the changes are automatically accounted for without none being left out. Further, not only is there a minimal effect on the production database system but also the schemas of the production tables need not be changed. There is also no need to add new tables. 

The only drawback of this method is that it works only with databases that support log-based CDC.   

Trigger-based CDC

This form of SQL Server CDC is based on triggers that are placed in the database. These triggers automatically react when any event or change occurs, thereby lowering the cost of extracting the changes. 

There is a wide range of benefits here. It can be easily implemented, details of the logs of all transactions can be found in the shadow tables, and direct support is received for selected databases in the SQL API.  

There are some downsides in this method too. In high operation cases the triggers get overloaded and disabled. Also, the performance of the database is adversely affected as this method needs several writes to a database every time there are changes made to the rows.   Moreover, there is an increase in the cost of running the source systems as additional runtime is required every time the database is refreshed. 

Summing up, SQL Server CDC is a big boost for organizations that depend largely on data-driven operations to run their business.