In this post, you will get to know in detail, the many intricacies of Microsoft SQL Server CDC (Change Data Capture), ranging from its inception, an overview, and how it works.
The Launch of the Microsoft SQL Server CDC Feature
In today’s business environment where data is the prime tool for any organization, the Change Data Capture feature assumes great importance as it ensures heightened data security and durability. This is not only about making sure that all data is insulated from breaches or hackers but also about securing change data so that their values are stored in a manner that does not compromise their history. Various solutions have been tried in the past in this regard like timestamps, triggers, complex queries, and data auditing but not with much success.
The first effective solution was offered by Microsoft in 2005 with their SQL Server CDC product. Its advanced features included “after update”, “after insert”, and “after delete” capabilities. However, this version did not find much favor with the database administrators who found it too complex. In 2008, Microsoft launched a revised version of the SQL Server CDC that became very popular. It enabled DBAs and developers to capture and archive changes and historical data without having to go through any other additional activities.
An Outline of Microsoft SQL Server CDC
SQL Server CDC uses the SQL Server to make changes like insert, update, and insert, details of which can be accessed by users in a simple relational format. For the modified and changed rows, all required inputs that are essential to capture the changes to a target ecosystem like metadata and column information are available. The changes made are thereafter stored in tables that mirror the structure of the columns of the tracked stored tables. Necessary table-valued functions control the access to this change data.
The ETL (Extract, Transform, Load) application is one of the best instances of a consumer targeted by this SQL Server CDC technology. Here, modified data from SQL source tables are incrementally moved by an ETL application to a data mart or a data warehouse.
How does the SQL Server CDC score over others? Normally, source tables in a data warehouse mirror all changes made to them but they have to be continuously refreshed. This can be a highly complex and tedious activity. On the other hand, a technology that ensures a smooth flow of change data that is structured to help users apply it to various target platforms is more appropriate. This is what SQL Server CDC does for organizations.
The Working of Microsoft SQL Server CDC
Any changes that are made by users in tables are tracked and monitored by Change Data Capture. These changes are later stored in relational tables that offer seamless access for quick retrieval of the data with T-SQL. Whenever CDC is applied to a database table, a mirror image of the tracked table is created. The structure of the columns of the replicated tables has additional columns of metadata that identify the changes made in the database rows.
Apart from this one aspect, the source tables and the replicated ones are similar in all respects. After the SQL Server CDC activity is completed, the new audit tablescan be used to monitor the logged tables and track all activities that have taken place.
The source of change in CDC is reflected in the transaction log of the SQL Server CDC. Immediately after any changes like insert, update, or delete is noticed in the tracked source tables, the details of these entries are added to the log and become a recommended component in CDC. This log containing all descriptions of the changes is then read and the changes are linked to the change table part of the original table.
Forms of Change Data Capture
There are two forms of SQL Server CDC.
The first is the Log-based CDC. Here, the transaction log and file of a database are analyzed by the system to know about the changes made at the source after which all changes made at the source are replicated to the target database. The main benefit of this form of SQL Server CDC is that it is very reliable with no possibility of missing out on any changes made. Moreover, there is also a minimal effect on the production database system. The schemas of the production tables need not be changed, nor is there a need to add new tables. However, the downside is that this method works only with databases that support log-based CDC.
The second form of SQL Server CDC is based on triggers placed in the database which automatically react when any event or change occurs, thereby lowering the cost of extracting the changes. On the other hand, there is an increase in the cost of running the source systems as additional runtime is required every time the database is refreshed.
There is a host of benefits in this trigger-based SQL Server CDC. For one, it can be easily implemented, details of the logs of all transactions can be found in the shadow tables, direct support is received for selected databases in the SQL API, and finally, the changes take place faster. As in the first type of CDC, there are some downsides too. Issues are faced in trigger overload and triggers become disabled during operations. 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.
Summing up, SQL Server CDC is a big boost for data-driven organizations.
