Data consistency is a critical aspect of data management. It helps ensure that analytics tools and applications use up-to-date, reliable, and accurate data. However, maintaining consistency can be challenging when dealing with large and complex data systems.

Fortunately, you can achieve real-time data consistency with Change Data Capture (CDC) technology. CDC captures real-time changes to your data sources and replicates them to target systems. This helps ensure that your data is consistent across all systems. 

This article will explain what is CDC, the different CDC approaches, the benefits of Estuary CDC, and how it can help achieve data consistency.

What Is Change Data Capture?

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Change Data Capture (CDC) is the process used in data replication systems to capture and replicate changes made to the source data in real time. CDC captures insert, update, and delete operations made on a source database. Then, it applies those changes to a target database or other downstream systems.

The CDC replication concept builds on the foundation of traditional ETL processes. CDC is often used as a data integration method. It ensures data consistency across multiple systems of an organization. Rather than replicating the entire data source for each run, CDC enables incremental loading of data. This helps save time and resources.

Different CDC Approaches

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There are three different CDC techniques. What’s common in these three techniques is the presence of a source and a target. The source is where the change occurs, and the target is where the updates must be reflected. Here are the three different CDC approaches:

Log-Based CDC

Almost all Database Management Systems maintain a transaction log file. This file records all the database changes made by each transaction. DML operations like CREATE, DELETE, and UPDATE are captured in the database log file. It also includes the time-stamp or database-specific unique identifier that indicates when each operation occurred.

Truly real-time CDC is performed using database logs. The technique uses the log information to spot changes and perform CDC operations. Apart from the source and target, an intermediate CDC platform is also involved in this method. This platform relays changes to the target using a message queue. The intermediate system monitors the event log, and when it detects a change in the source, it sends the event to the target via the message queue. The target consumes the message almost instantaneously.

Since log-based CDC directly accesses the logs, it’s a non-intrusive technique. It doesn’t place additional load on the source system and offers impressive performance. 

If you have a large-scale database or require instantaneous data replication, consider using log-based CDC.

Trigger-Based CDC

The trigger-based technique uses database triggers to identify any changes in the source system. To implement this, you must write trigger functions to monitor updates, inserts, and deletes. These triggers are used to create a change log, following which the changes are captured into the target database.

Since you must place the triggers on the source system’s tables, this requires modifying the source system, making it an invasive technique. Trigger-based CDC implementation is specific to the database on which you need to create the triggers. You can implement triggers on the SQL level and get almost immediate outputs.

If you deal with systems lacking database transaction logs or you want more control over data capture, trigger-based CDC is an ideal choice.

Query-Based or Time-Based CDC

For the query-based CDC method, it’s required for the source table to have a timestamp column. The timestamp field helps identify and extract the changed data sets. The process involves simple SQL queries on the source tables. These SQL queries identify changes to a table by selecting those records with a timestamp value exceeding that of the last-run value. The selected records will be used to update the target.

The query-based CDC is less efficient than log-based or trigger-based CDC and has a few shortcomings.

• If you perform a hard delete on any table records, the query won’t detect the deleted data.
  
• Depending on your query frequency, there will be some lag between data updates in the source and target. This latency is because the query method is a form of batch CDC.
  
• For every query run, you poll the entire table. You’re likely to run into source database performance issues for larger tables.

If the data changing frequency in your system is low or when the other CDC options aren’t viable, consider using query-based CDC.

What Is Data Consistency and Why Is It Important?

Data consistency refers to the uniformity, completeness, and accuracy of data across multiple databases, applications, and systems. For data to be consistent, it should be the same and up-to-date across all the locations where it’s stored and accessed.

Data consistency is essential for any data-driven business. It ensures that the data that you use to make decisions is accurate, up-to-date, and reliable. Inconsistent data can lead to problems with data analysis, data integration, and data migration. This could be time-consuming and costly. Using inconsistent data can also result in incorrect decisions, inaccurate reports, and a loss of credibility.

In the past, the common approach for achieving data consistency was by using traditional batch processing. It involves collecting, transforming, and loading data in batches at specific intervals. This process comes with limitations like high latency and potential data loss. Hence, batch processing is unsuitable for real-time applications that require data consistency.

This is where CDC technology comes to the rescue. You can achieve real-time data consistency by using CDC.

How Does Estuary’s CDC Work?

Estuary’s DataOps platform, Estuary Flow, lets you harness the power of CDC replication. You can use Flow to manage and optimize your data pipelines. This makes it easier to adopt CDC replication and reap its benefits.

Estuary’s CDC works by capturing changes made to a database’s data and structures at the transactional level. For any change, like update, delete, or insert, Estuary captures the change and sends it to a message queue. From the message queue, the change is propagated to other systems in real time. This process ensures that all integrated systems have the same data at any given time, ensuring data consistency across the entire organization.

Benefits of Estuary’s CDC

There are several benefits of using Estuary's CDC. Here’s a list of the benefits:

Real-time Data Consistency

Estuary CDC technology allows you to achieve real-time data consistency across all of your organization’s systems. It achieves this by ensuring that all the systems have the same data at the same time. This also helps prevent data inconsistencies that could result in errors or data loss.

Supports Various Databases and Message Queues

Estuary CDC supports different databases, including MongoDB, MySQL, PostgreSQL, and Oracle. It also supports different message queues like Amazon Kinesis and Apache Kafka. This ensures that you can use Estuary CDC with your existing systems.

Estuary provides a wide selection of pre-built connectors. You can use these connectors for quick source-to-destination database connections. All you must do is fill out a few fields to set up the endpoint configurations.

Easy to Setup and Use

Estuary CDC is easy to set up and use. You can use Estuary’s simple and intuitive user interface that allows users to set up and configure CDC easily.

You can use Estuary to build a complete end-to-end data pipeline in just a few clicks. The two steps involved in building a pipeline include:

• Capture: Used to connect to the source
  
• Materialization: Used to connect to the destination

With Estuary’s simple UI, you need only to fill in a few fields, and a few clicks later, your pipeline will be ready.

Low Latency Streaming

Estuary provides low-latency streaming in near real-time, with a mere milliseconds-equivalent latency. Streaming is when action is taken on a series of data at the time of data creation. This allows data replication in near real-time with low latency, as is seen with Estuary.

The other type of processing is batch processing. It requires you to schedule when the replication must take place over a predetermined interval of time.

You can use Estuary’s real-time streaming for high volumes of data that are frequently updated. 

Minimal Impact on Database Performance

Estuary captures changes at the transactional level. This minimizes the impact on the database’s resources, minimizing the impact on the database’s performance.

Affordable Pricing

Estuary offers transparent and affordable pricing. The first 10GB of data is free, and thereafter, it’s $0.75/GB. This means after the initial free load of data, the cost applies only to incremental changes. Considering your monthly replication of data is under 100GB, you’ll incur charges of about $75 or less. If you were to use most of the other available platforms for data replication, it would cost you thousands of dollars each month.

Scalability

Estuary’s CDC is designed to handle large volumes of data. The technique includes a distributed architecture that enables it to handle high volumes of data with ease.

Best Practices For Implementing CDC

Here are some best practices for CDC to ensure optimal performance, system stability, and data consistency:

• Select the right CDC approach: Consider your team’s skill and expertise alongside your organization’s resource and budget constraints. Choose a method that is scalable, has resources for support, and can be integrated with your existing systems.
  
• Preserve the order of changes: It’s essential that the order of the changes be preserved for change data capture. This allows data propagation to the target system in the same format.
  
• Handle schema changes: For a seamless CDC process, you’ll require effective schema change management. Else, poorly managed schema changes can result in data inconsistencies, corruption, and system failures. Either automate schema change detection and adaptation or invest in CDC solutions that can handle schema changes.
  
• Light-weight transformations: Ensure the CDC process supports lightweight message transformations. This is important since the event payload must match the input format of target systems.

Conclusion

Data consistency is undoubtedly one of the most important aspects of data management. Estuary’s Change Data Capture (CDC) helps achieve real-time data consistency with low latency streaming, is easy to use, scalable, and has several built-in connectors. You can use Estuary CDC replication to ensure the availability of accurate data to analytics tools and applications.

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