Every organization wants to stay on the cutting edge of technology, making smart and data-driven decisions. Microsoft SQL Server data collection will help capture valuable information and save it in a relational database or data warehouse for analysis.

However, ensuring that company information and data integration remains fresh and relevant can be a very time-consuming process. That’s where CDC can make all the difference.

Change data capture or CDC ensures that enterprise data is always up-to-date and can make companies’ data workflows dramatically faster and more efficient. So what is change data capture, exactly, and what are the different types of CDC? Also, how is CDC implemented in SQL Server, and how does it integrate with ETL? Read on to discover the answers to these questions.

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Table of Contents 

What is Change Data Capture?

Change data capture, or CDC, is a set of software processes and techniques that identify changes in source tables and source databases, and then transfer those database changes. When companies use CDC, they can usually detect changes in real-time. In most cases, the changed entries move through data replication to a specific target location; companies can then use this updated data for business intelligence (BI) and data analytics workflows. 

Time is money when it comes to business. Change data capture is an ideal solution for companies looking to work with data more efficiently, as CDC works in real-time movement. After data collection in SQL Server, CDC helps move this information to a data warehouse, data lake, or other databases in real-time or near-real-time. The data movement efficiency that change data capture provides is extremely beneficial for organizations. 

Related Reading: What is Change Data Capture?

Types of Change Data Capture 

CDC has two main types. First, companies can perform change data capture by log-based CDC; second, they can use trigger-based CDC. 

1. Log-based CDC 

In log-based CDC, the change data capture solution examines a database’s transaction log. During this process, the CDC solution reads the file to uncover the source system changes. This metadata information is stored in CDC change tables. Then, it executes data replication of these source changes to the target data store. 

The pros of log-based CDC are:

  • High reliability with no missed changes.
  • Minimal impact on the production database system.
  • No requirements to change the production database's schemas or the need to use additional SQL Server CDC tables.

The cons of log-based CDC are:

  • Works only with databases that support log-based CDC.
  • High complexity.

2. Trigger-based CDC 

In trigger-based CDC, the change data capture solution uses database triggers. During this process, the CDC solution runs when another event occurs. These database triggers can decrease the overhead that results from extracting changes. However, they also add overhead to the source systems because they require a certain amount of run time each time the existing database refreshes. 

The pros of trigger-based CDC are:

  • Easy implementation.
  • Changes can happen quickly.
  • Shadow tables can provide a detailed log of all transactions.
  • Receives direct support in the SQL API for some databases.

The cons of trigger-based CDC are:

  • Can experience trigger overload.
  • Triggers may be disabled during certain operations.
  • Significantly reduces the overall performance of the database by requiring multiple writes to a database every time a user inserts, updates, or deletes a row.

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Change Data Capture and ETL

The real perk of change data capture is that it can save companies from focusing on unnecessary data work. By implementing MS SQL CDC, the company ensures that its system only concentrates on new updates to records, rather than an entire SQL Server database. 

The benefits of change data capture can also prove useful for ETL. The most commonly used and dominant type of data integration is ETL or Extract, Transform, Load. In ETL, the process extracts information from one or more data sources, cleans it, and transforms it as necessary. Finally, the process delivers that information to a data warehouse, data lake, or other database types. 

Related Reading: What is ETL?

Pairing SQL change data capture with ETL has the potential to save companies a great deal of time and effort, compared to running a traditional ETL system. This is because traditional ETL has a slow transformation step. Change data capture SQL Server solutions help improve the time required to carry out the data transfer and decrease the resources required for the entire ETL process. 

Related Reading: ETL vs ELT: 5 Critical Differences

Implementing CDC in SQL Server 

Now that we've discussed the types of change data capture and how they relate to ETL, it’s time to look at how to implement CDC in SQL Server. Change data capture records, inserts, updates, and deletes activity that applies to an SQL Server table, which means organizations can capture changes in SQL Server data by using the SQL Server change data capture feature. 

However, the data system must meet certain prerequisites before you can enable CDC SQL Server. These prerequisites include:

  • Having “sysadmin” privileges 
  • Running SQL Server Developer, Enterprise, or Standard Edition, as the web does not support CDC functionality 
  • Ensuring the SQL Server Agent runs on an SQL Server instance

Once the company system meets these prerequisites, the user can use the following steps to implement SQL Server change data capture. 

  1. Open the SQL Server Management Studio and create a database. 
  2. Create a table. 
  3. Enable CDC on the database. 
  4. Define the specific table on which to enable change data capture.
  5. Insert the values into the table. 
  6. Verify that the change data capture is working. 

What does SQL CDC look like under the hood in terms of technical implementation? The answer can get a little messy and technically involved:

  • dbo.cdc_jobs is a Microsoft SQL Server system table that keeps track of the CDC parameters for capture jobs and cleanup jobs. This includes settings such as the database ID, the type of job to run, the number of seconds between polling for new data, and much more.
  • sys.sp_cdc_enable_db is a stored procedure for enabling CDC on the given database, while sys.sp_cdc_enable_table turns on CDC for a given table. To run these procedures, type “exec sys.sp_cdc_enable_db” and “exec sys.sp_cdc_enable_table”, respectively. These commands take multiple arguments: for example, source_name is the table name of the source on which you are enabling CDC, and role_name is the name of the role that controls access to change data.
  • Implementing CDC in SQL Server will also require familiarity with DDL (Data Definition Language) and DML (Data Manipulation Language) SQL commands.

Ultimately, by implementing MSSQL CDC, organizations can reduce the time spent on data integration tasks and ensure the system handles information more efficiently, changing data in real-time. 

CDC and SQL: How Integrate.io Can Help 

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Is your company in need of a simple yet efficient solution for ETL and SQL Server CDC? We can help. Integrate.io is a powerful and feature-rich yet user-friendly cloud-based ETL (Extract, Transform, Load) solution. The Integrate.io platform's drag-and-drop interface makes it easy to build data pipelines for automated data flow across various sources and destinations. 

Thanks to Integrate.io’s code-free data integration, everyone from data professionals to non-technical team members can build rich data pipelines. Whether you’re a developer or non-developer, your company can benefit from using CDC and ETL solutions. Contact our team today to schedule a 7-day pilot and see how we can help you reach your CDC ETL goals.