3 Must-Know CDC Methods

Database replication is a necessary headache for many companies. According to Statista, the total amount of data created, captured, copied, and consumed globally is forecasted to reach 149 zettabytes by 2024. TReplication can require hours of work and downtime. 

Change Data Capture is a top method for optimizing database replication and streamlining ETL (extract, transform, and load). Instead of replicating the full database, CDC only replicates the latest changes. What do you need to know about CDC in data engineering and what are the main methods and benefits?

What Is CDC?

Simply put, CDC software tracks changes in a database. In this way, your ETL software can extract data as it is written. Users can set criteria for what data the software 'captures' into individual files. CDC is essential for real-time updates from data source systems. It is an alternative to batch processing and instead relies on stream processing. Integrate.io can use this method to provide a better data analytics experience. 

Data replication is essential to load data into specialized BI (business intelligence), ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) software. Since you do not need to replicate the entire database if you are using CDC, BI becomes much simpler and less time-consuming. 

CDC is a well-known process and used by major players such as AWS, Oracle and Microsoft, yet many companies still rely more heavily on batch processing than is necessary. These companies need to reconsider how they handle big data. Some of the benefits of using CDC tools include the following:

Benefits of CDC

Businesses have limited resources to dedicate to ETL and data management. CDC solutions allow users to reduce the time and cost required.

• Speed. Real-time and analytics rely on CDC because it enables rapid and actionable updates to databases by providing real-time data or nearly real-time data. Many forms of AI and machine learning depend on CDC for better and faster results. Since you can update BI and other analytical tools on a real-time basis, you can also get to actionable insights faster and make more informed decisions. 
• Reliability. You can reduce latency and downtime through CDC as well. Batch processing requires heavy CPU usage and resource dedication that often disrupts normal functionality. Change Data Capture tools can help you treat your clients better by eliminating some of the pain points associated with accessing your services during database changes.
• Minimized cost. Many databases and ETL solutions charge depending on the amount of data being processed and the CPU required. Since CDC software allows you to process data much faster and in smaller amounts without requiring full database replication, reducing workloads, costs tend to be much lower. 

Integrate.io lets you capitalize on these benefits through no-code and low-code ETL, allowing even faster data transfers. The platform allows you to build data pipelines for optimized analytics, data integration and CDC.

Top CDC Methods

What are some of the top three must-know ETL CDC methods you can implement in your business? There are many different ways to implement CDC and they vary depending on the company and particular use case. Three of the most common methods for Change Data Capture include the diff-based method, the trigger-based method, and the log-based method.

1. Diff-Based Method

CDC is all about tracking changes in data. This method, also called table differencing, is when the system selects and loads only new data that differs from the original source. Algorithms comparing sources with targets let you ensure all changes are documented. Various types of software have built-in settings to do this, or you can sometimes utilize SQL server scripts for the same purpose. Most sources agree that this method builds strain on the CPU, requiring greater processing power than some other methods.

2. Trigger-Based Method

CDC software can trigger a log entry upon any new commands changing or updating the data. This makes CDC a two-step process: either triggering and then performing the transaction or vice-versa. In both cases, the possibility of doubling processing time may cause many to shy away from this option. Since every source table needs a trigger, overhead may be greater for this method than for most. Also, different solutions may need different configurations to implement database triggers.

3. Log-Based Method

Log-based CDC is the top method due to the cost and time-consuming nature of most other methods. Transactional databases log changes in case of a crash, so the transaction log method simply makes use of a feature already included in the database without any additional configuration. While log files may be the go-to method for some, it does have a few disadvantages. The main drawback of log-based CDC is that documentation and interpretation can be limited and difficult since every database is different and there is no standardized change log transaction across databases. Also, the type of changelog that is native in a database may not have all the information required to get the full benefits of CDC.

Other Methods for Performing CDC

You are not only limited to these three capture data options. Additional methods include the timestamp-based method and the script-based method. The timestamp or DATE_MODIFIED method uses a timestamp column to track exactly when any changes are made to the data. It also must be applied across all tables. The script-based method requires a developer to alter schema and develop application-level data change indicators. It is particularly costly since you have to pay an industry expert to specially configure your CDC. Most companies use a mixture of all or several of these methods to update their data stream constantly and gain the greatest benefits.

CDC and Integrate.io

CDC allows you to capture changes in a source database in real-time or at least constantly and at great speed. It is not always necessary, but it is an extremely useful tool. Understanding and using this asset will help you become more effective throughout your business processes. Paired with the power of Integrate.io ETL, Change Data Capture becomes even more useful. Integrate.io has built-in CDC features for easy implementation, giving it functionality far beyond what you can find in native database log systems. The platform also offers over 100 native connectors to all the most popular database solutions. With extensive documentation and support in addition to a simple drag-and-drop UI, Integrate.io is easy to use and highly efficient. Change Data Capture is part of what you need to bring your data management strategy to its highest level. You can achieve that with Integrate.io ETL CDC pipelines. To learn more about what the platform can do for you, reach out today and schedule your seven-day trial.

FAQs

Q: What is the ETL Process in CDC?

The ETL (Extract, Transform, Load) process in Change Data Capture (CDC) involves:
1. Extract: Capturing changes (inserts, updates, deletions) from source systems in real-time or near-real-time.
2. Transform: Processing these changes to fit the schema and requirements of the target system.
3. Load: Applying the transformed data to the target system, ensuring synchronization and consistency between source and destination systems.

CDC enhances ETL by focusing only on incremental changes, improving efficiency and supporting real-time data integration.

Q: What Are the Different Types of CDC?

The main CDC types are:
1. Query-Based CDC: Periodically queries the source database for changes since a specific timestamp. It is simple but less efficient due to frequent database queries.
2. Trigger-Based CDC: Uses database triggers to capture changes as they occur. While efficient, it can increase database overhead and complexity.
3. Log-Based CDC: Monitors database transaction logs to capture changes in real-time. This method is highly efficient but requires access to and parsing of transaction logs.
4. Polling-Based CDC: Regularly polls the source system for changes but is suited for batch processing rather than real-time needs.

Q: What Are the Methods of ETL?

ETL processes can be implemented using several methods:
1. Batch Processing: Extracts, transforms, and loads data in scheduled intervals (e.g., nightly or weekly).
2. Real-Time ETL: Processes data as it arrives, often using streaming technologies.
3. Incremental ETL with CDC: Focuses on capturing and processing only the changed data, reducing resource usage and latency.
4. Push vs Pull Operations:
   - Push: Source systems push data to the ETL pipeline.
   - Pull: The ETL system pulls data from source systems periodically.