Overview of Change Data Capture

Change data capture is a data management process designed to capture, track, and quickly move data as it changes. Unlike other traditional processes that batch replicate data once or several times a day, CDC allows organizations to replicate data within milliseconds and make decisions based on the latest data. can do. This increases the efficiency and productivity of your organization’s critical business operations, giving your organization a competitive edge.

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CDC is especially effective in cloud migrations. With low latency and independent monitoring of changing data, enterprises can analyze newly generated data without compromising operational database performance. This overview of change data capture explains how it works, why it’s important, and useful tools for managing CDC.

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What is change data capture?

Change data capture is the process of recognizing and monitoring database data changes and movements. With CDC, data is often transferred in small increments from one database to another.

Traditional data movement is bulk-based, typically using ETL tools to move data from source to destination. The challenge with this method is the limited batch window or time period over which data can be moved.

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Change Data Capture takes a different approach. All changes or transactions are captured in real time and moved from the source database to the target database in small chunks.

There are three main methods used in change data capture.

Log-based CDC

All databases create a log file each time a new transaction occurs. A CDC solution that uses a log-based approach can therefore read the log files, retrieve these changes, and apply them to the target database. This method is very efficient and has no impact on the source system.

Query-based CDC

CDC solutions that use a query-based approach rely on running specific queries against the source. For example, this type of CDC solution can look at time stamps to determine which records have changed. Then read those changes and apply them to the target database.

Trigger-based CDC

A trigger is a piece of code that fires when certain conditions are met. This launches a change data capture solution that is triggered whenever a change is made to the source database. The trigger then picks up the changes and applies them to the target database.

Why Change Data Capture Matters

Change data capture is important because it allows organizations to move data in real time without impacting the performance of the source database. This allows changes and updates to be reflected quickly and accurately in the target database.

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Additionally, change data capture helps improve overall business operations and data management. By responding instantly to change, businesses can make more informed, data-driven decisions about their operations.

Benefits of CDCs

CDC is growing in popularity among data teams managing large databases. From reduced bulk load sizes to more efficient data transfers, there are many benefits that make it an attractive option for database managers and administrators. Below, we examine some of the key benefits of using change data capture in your database environment.

Efficiency and impact reduction

Change data capture eliminates the need for eager load updates and inconvenient batch windows. CDC enables real-time streaming of data changes to desired repositories, requiring only incremental loads.

Log-based CDC in particular is very efficient because it only captures changes instead of scanning the entire table every time data needs to be transferred. This CDC approach can significantly reduce the impact on the source.

In addition, by using CDC to replicate data on the fly, you can migrate your database without issue and perform analytics in real time. Finally, CDC facilitates fraud prevention and synchronizes data across databases around the world.

Cloud optimization

CDC is an efficient way to move data across wide area networks, making it ideal for cloud use and can be used to quickly move large amounts of information between on-premises and cloud databases. This makes it an ideal solution for businesses looking to migrate databases to the cloud or utilize hybrid deployments with both on-premises and cloud components.

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It is also ideal for migrating data to stream processing solutions such as Amazon Kinesis Streams and Apache Kafka. CDC’s compatibility with stream processing technology allows enterprises to take advantage of real-time analytics without sacrificing performance or scalability.

data sync

CDC also ensures that data across multiple systems is always synchronized. As an example, CDC is especially important for time-sensitive applications dealing with financial transactions where accurate data synchronization is paramount.

With CDC, you don’t have to worry about discrepancies between different databases. Changes made are automatically propagated to all connected systems, ensuring that all users always have access to the latest information. This makes it ideal for customer relationship management solutions that require near real-time updates across multiple platforms.

Examples of CDC solutions

Several change data capture solutions are available, ranging from open source to proprietary. Below are some common change data capture solutions.

Oracle GoldenGate

Oracle GoldenGate is efficient CDC and replication software that enables users to easily move data from one database to another without errors or delays. Oracle GoldenGate enables optimized high-speed data movement and replication for Oracle Database. It also supports a wide range of other sources including Microsoft SQL Server, IBM DB2, Teradata, MongoDB, MySQL and PostgreSQL.

Oracle GoldenGate enables end-to-end monitoring of stream data processing solutions while reducing the need to manage computing environments. Its ease of use, fast data movement capabilities, and availability on multiple platforms have made it a popular CDC option.

talent

Talend is the leading data integration software for enterprise-level CDC. Talend’s product offerings range from the company’s flagship open source platform, Open Studio for Data Integration, to Talend Integration Cloud, with three independent editions that offer broad connectivity and superior built-in cloud capabilities. I’m here.

Talend’s integrated big data components and connectors provide seamless access to a wide variety of popular technologies such as Hadoop, NoSQL, MapReduce, Spark, and various machine learning and IoT solutions. Talend’s CDC replication service offers reliability, scalability, and rapid adoption for any company looking to update their data management processes.

Qlik Replicate (formerly Attunity Replicate)

Qlik Replicate is an advanced log-based change data capture solution that you can use to streamline data replication and ingestion. It focuses on speed by using parallel threads to process large amounts of data quickly.

Qlik provides connectivity between major data sources such as RDBMS platforms, data warehouses and cloud vendors such as AWS, GCP and Azure. Its flexible connectivity options make Qlik Replicate a scalable solution for cross-integration. Qlik Replicate enables real-time replication of data changes, ensuring that the same changes are immediately applied to target endpoints.

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