Change Data Capture (CDC) identifies and records data modifications in real-time as they happen within source systems, like databases or applications.
Introduction to Change Data Capture (CDC):
Change Data Capture (CDC) plays a vital role in modern data-driven environments by ensuring real-time data consistency and seamless integration across various cloud environments. It identifies and records data changes instantly as they happen in source systems like databases or applications. This method enables businesses to stay updated with the latest information, facilitating timely decision-making based on current data, which traditional batch processes cannot achieve due to inherent delays.
Methods of Change Data Capture:
Change Data Capture (CDC) employs different methods to capture data changes, each with its unique approach. These methods include log-based CDC, trigger-based CDC, and timestamp-based CDC.
Log-based Change Data Capture:
This approach entails monitoring the transaction logs of the database to track changes. Whenever a change like insertion, update, or deletion occurs, the log captures the change and records both the previous and updated data states. This modified data is then transmitted to downstream systems for further processing. Log-based CDC is efficient because it only captures data that has changed since the last replication, minimizing the volume of data needing processing.
Trigger-based Change Data Capture:
In this method, database triggers are utilized to detect data changes. Whenever a change occurs, such as insertion, update, or deletion, the trigger identifies the change and transmits it to the downstream systems. Trigger-based CDC requires careful configuration and management of database triggers to ensure that all relevant changes are accurately captured and transmitted.
Timestamp-based Change Data Capture:
This approach utilizes timestamps to monitor data alterations. Every database row receives a timestamp upon creation or modification. When changes occur, the timestamp is updated, and the modifications are transmitted to downstream systems. Compared to log-based and trigger-based CDC methods, timestamp-based CDC is less intrusive, as it doesn’t necessitate monitoring transaction logs or configuring database triggers. However, it requires careful timestamp management and may not suit all types of data changes.
Applications of Change Data Capture:
CDC is extensively utilized across various sectors. For example, in HR operations, CDC captures real-time changes in employee data. This enables downstream systems like data warehouses, data lakes, and analytics platforms to consume and process the latest updates promptly. By facilitating real-time data integration, synchronization, and analysis, CDC empowers organizations to enhance vital HR processes such as payroll, performance management, and workforce planning.
Advancements in CDC: Introduction to Flink CDC:
The advancement of CDC has given rise to sophisticated tools like Flink CDC, which address the complexities of contemporary data processing. Flink CDC serves as a distributed data integration tool designed for both real-time and batch data processing. It introduces a fresh approach to data integration using YAML, simplifying the description of data movement and transformation. This tool aims to streamline the intricate field of data integration, making it a favored choice for industry professionals.
Key Features and Benefits of Flink CDC:
Distributed Historical Data Scanning: Flink CDC stands out in scanning historical data across distributed systems and smoothly transitioning to capture changes. It utilizes an innovative incremental snapshot algorithm, ensuring that switching to CDC doesn’t lock the database, thus maintaining system availability and performance.
Schema Evolution:
One notable feature of Flink CDC is its ability to automatically generate downstream tables based on inferred structures from upstream tables. It also applies upstream Data Definition Language (DDL) to downstream systems during the change data capture process, facilitating seamless and automatic adaptation to schema changes.
Streaming Mode Operation:
Flink CDC operates in streaming mode by default, offering leading sub-second end-to-end latency in real-time binlog synchronization scenarios. This ensures downstream businesses have access to the most up-to-date data, enabling timely decision-making with confidence.
Data Transformation:
Flink CDC is poised to introduce support for various Extract, Transform, Load (ETL) operations, including column projection, computed column, filter expression, and classical scalar functions. This empowers users to perform complex data manipulations directly within the CDC process, enhancing its versatility and usefulness.
Full Database Synchronization:
The tool provides the capability to synchronize all tables of a source database instance to downstream systems in a single job. By configuring the captured database list and table list, it streamlines data synchronization and reduces the complexity of managing multiple jobs.
Exactly-Once Processing:
Flink CDC ensures exactly-once processing of CDC events, even in the event of job failures. This feature guarantees data integrity and consistency, offering users peace of mind that their data is accurate and reliable.
Benefits of Change Data Capture with Alibaba Cloud:
Alibaba Cloud’s Flink Change Data Capture (CDC) presents a range of open-source connectors that adhere to the Apache Flink protocol. These connectors seamlessly integrate with Alibaba Cloud’s Realtime Compute for Apache Flink platform, enabling organizations to capture data changes from a variety of databases like MySQL, PostgreSQL, MongoDB, and others. This ensures real-time data integration and synchronization across a wide array of data sources.
The primary advantage of utilizing CDC with Alibaba Cloud lies in its capacity to seamlessly support a broad spectrum of databases and data formats, enabling organizations to seamlessly integrate and analyze diverse data sources. Moreover, the open-source nature of these connectors allows for customization and extension to suit specific business needs. Additionally, Alibaba Cloud’s fully managed service ensures that organizations can prioritize their core business activities while entrusting the complexities of infrastructure management to Alibaba Cloud. This encompasses automatic scaling, continuous monitoring, and comprehensive security features, enabling businesses to harness the benefits of real-time data integration without the burden of maintaining their own data infrastructure. YVOLV, a joint venture of Alibaba Cloud in the MENA region, further enhances the capabilities and accessibility of these services to organizations in the region.
