Every organization wants to stay on the cutting edge of technology, making smart and data-driven decisions. However, ensuring that company information and data integration remains up to date can be a very time-consuming process. That is where CDC can make all the difference. Change data capture or CDC allows for real-time data set changes, ensuring that company data is always up to date. 

Change data capture can transform the way companies make data-driven decisions. So what is change data capture, exactly, and what are the different types of CDC? Also, how is CDC implemented in an 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 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. Through CDC, data travels from relational databases like SQL Server to a data warehouse, data lakes, 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. 

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; then, it executes data replication of these source changes to the target data store. 

Pros of log-based CDC:

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

Cons of log-based CDC:

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

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. 

Pros of trigger-based CDC:

  • 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

Cons of trigger-based CDC:

  • 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 CDC, the company ensures that its system only concentrates on new updates to records rather than an entire 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. 

Pairing CDC 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 the traditional ETL has a slow transformation step. The change data capture improves the time required to carry out the data transfer, decreasing the resources required for the entire ETL process. 

Related Reading: What is ETL?

Related Reading: ETL vs ELT: 5 Critical Differences

Implementing CDC in SQL Server 

Now that we've discussed the types of CDC and how they relate to ETL, it’s time to look at how to implement change data capture in an 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 it can implement change data capture in 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 change data capture in SQL Server. 

  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. 

Ultimately, by implementing change data capture in SQL Server, 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 Xplenty Can Help 

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Is your company in need of a CDC, SQL, and ETL solution to run more efficiently? Xplenty can help. Xplenty is a user-friendly cloud-based ETL (Extract, Transform, Load) solution providing simple visualized data pipelines for automated data flow across various sources and destinations. 

The code-free data integration of Xplenty allows data professionals to build rich data pipelines. Whether you are a developer or non-developer, your company can benefit from using CDC and ETL solutions. Contact our team today to schedule a 14-day demo or pilot and see how we can help you reach your goals.