The prime need of today’s data-driven organizations is enhanced data durability and security, attributes that are ensured by the Change Data Capture (CDC) technology. Apart from ensuring that data is insulated from the possibilities of breaches and hacking, CDC also makes sure that change data is stored in a way that does not affect its history. To this end, several solutions have been tested in the past like complex queries, timestamps, and triggers, but none has met the level of success achieved by the Change Data Capture feature.
Launch of SQL Server CDC
It was Microsoft that, in 2005, first launched the SQL Server CDC product with advanced and cutting-edge features that carried the “after update”, “after insert”, and “after delete” abilities. But this technology did not have the concurrence of the DBAs as it was found to be very complex and invasive. About three years later in 2008, a revised version of SQL Server CDC that was a vast improvement over the previous technology was introduced. It helped developers and DBAs to capture and document historical data without any other activities or work.
The Technology Behind SQL Server CDC
The SQL Server is used by the SQL Server CDC for making changes such as insert, update, or delete, and users can access all details of them in an easy-to-understand relational format. Every input that is required to capture the changes to a target environment like column information and metadata for modified and changed rows are available in CDC. All changes made are then stored in tables and these reflect the structure of the columns of the tracked source tables. Access to the change data is controlled by the required table-valued functions.
One of the best examples of a consumer who is a target of this SQL Server CDC technology is the ETL (Extract, Transform, Load) application. The change data from the source tables of SQL is moved incrementally by an ETL application to a data warehouse or data mart.
How is SQL Server CDC a cut above the others in this field? Usually, all source tables in a data warehouse mirror the changes made to them and need to be refreshed at all times, a process that can be very tedious and time-consuming. A technology, on the other hand, is more appropriate when it allows a smooth flow of changed data that is structured in a way that helps users apply it to various target platforms. This is how SQL Server CDC works for organizations.
The Functioning of Microsoft SQL Server CDC
Change Data Capture tracks and monitors all changes that are made in tables by users and these are then stored in relational tables that offer quick access for easy and seamless retrieval of the data with T-SQL. In all instances where CDC is applied to a database table, another mirror image is created of the tracked table. The structure of the columns of the replicated tables also has added columns of metadata that recognizes the changes made in the database rows.
Apart from this difference, all other aspects of the source and the replicated tables are the same. After the completion of a specific SQL Server CDC activity, the new audit tables may be used to track the logged tables and monitor all work that has taken place. The transaction logs of the SQL Server CDC reflect the source of the changes in CDC.
As soon as changes like insert, update, or delete are noticed in the tracked source tables, the details of these entries are added to the log, thereafter becoming an integral component of CDC. The log with all detailed descriptions of the changes is then read after which they are linked to the change table part of the original table.
Types of SQL Server CDC
There are two forms of Change Data Capture.
In this method, the file and the log of a database is analyzed by the system. It is to know about all the changes that are made at the source which are then replicated to the target database. The primary benefit of this type of SQL Server CDC is that it is very accurate with no possibility of missing any changes made. Further, the effect on the production database system is negligible as the schemas of the production tables need not be changed nor do new tables have to be added.
However, there is a downside to this method and that is, it works only where the databases have the support of the log-based CDC.
This form of SQL Server CDC works through triggers that are placed in the database. These react automatically when any change or event occurs, thereby significantly reducing the data extraction costs. However, there is a simultaneous increase in the cost of operating the source systems as more runtime is needed every time the database is refreshed.
There are several benefits and downsides of this trigger-based SQL Server CDC.
The benefits include easy implementation of this process, finding details of the logs of all transactions in the shadow tables, and receiving direct support for selected databases in the SQL API. The changes happen faster too in this method.
Coming to the downsides, the most critical is that issues are seen in case of trigger overloads and they might become disabled during operations. Further, the performance of the databases might be adversely impacted as this method needs many writes to a database whenever there are changes made to the rows.
In a nutshell, then, SQL Server CDC is a blessing in disguise for data-driven businesses.