- What is Change Data Capture (CDC)?
- What are the Different CDC Techniques?
- How Does CDC Work?
- What are the Types of CDC?
- What are the Components of CDC?
- What are the Benefits of CDC over ETL?
- How CDC Improves Data Quality?
- How CDC Helps in Data Integration?
- How CDC Is Advantageous Over Other Data Processing Techniques?
- How We Can Help
What is Change Data Capture (CDC)?
Change Data Capture (CDC) is a widely applied design pattern in data warehousing that identifies, captures, and delivers changes made in the source database to downstream systems.
As a process, CDC is imperative in capturing only the changes made to the data, reducing the resources required to reflect these changes in other systems. This approach is typically used in data replication, warehousing, and integration scenarios to facilitate data updating efficiently and on time.
When deployed effectively, CDC can significantly enhance the efficiency and speed of data transfer, reducing latency and improving overall data management.
From a business perspective, CDC offers a range of benefits.
Near real-time data updates enable businesses to make informed decisions based on up-to-the-minute data.
Also, CDC minimises the impact on the source systems by reducing the need for full data loads, thus enhancing system performance and ensuring data consistency and integrity across various systems. This is crucial for maintaining data quality and accuracy.
What are the Different CDC Techniques?
Organisations can deploy several change data capture techniques depending on their needs and requirements. The most common techniques include trigger-based, log-based, and snapshot-based CDC.
Trigger-based CDC involves creating database triggers that capture changes in data whenever an insert, update, or delete operation is performed. The captured changes are then stored in a separate table from where they can be retrieved and processed.
Log-based CDC, on the other hand, captures changes by reading the source database’s transaction log.
This approach is typically less intrusive than the trigger-based method as it does not alter the database schema or impact its performance.
Finally, snapshot-based CDC involves taking periodic snapshots of the source data and comparing them to detect changes. This technique is generally used when the source system does not support triggers or transaction logs.
How Does CDC Work?
CDC operates on a basic principle: capturing and processing data changes as they occur. It begins with recording initial data snapshots followed by recording changes that happen to the data over time. These changes could include updates, insertions, and deletions made in the operational databases.
To achieve this, CDC utilises high-speed data processors and sophisticated algorithms that track changes at the data source.
Depending on the chosen CDC method, these changes are replicated in real time or at scheduled intervals. The captured data changes are stored temporarily in a change table before being pushed to the target system or data warehouse.
Performing this process ensures that the target systems are updated with the most recent and relevant data changes, thereby maximising data currency and relevancy.
What are the Types of CDC?
The two main types of CDC are real-time CDC and batch CDC. Real-time CDC, also known as streaming CDC, captures and delivers data changes as soon as they occur.
This type provides a continuous flow of data changes to the target system, greatly reducing data latency. This type is especially useful in scenarios requiring immediate data availability, such as fraud detection or real-time analytics.
On the other hand, batch CDC captures changes over a specific period and delivers them to the target system in bulk. This type is usually used when the target system can tolerate some latency and the volume of data changes is not too high.
What are the Components of CDC?
The primary components of CDC include the source database, the CDC process, and the target system. The source database is where the original data resides and where changes occur. The CDC process detects, captures, and delivers these changes to the target system, which is often a data warehouse or another database.
The CDC process itself may comprise various sub-components depending on the technique used. For example, in a trigger-based CDC, the triggers are a crucial component, while in a log-based CDC, the transaction log is key.
What are the Benefits of CDC over ETL?
CDC offers several advantages over traditional Extract, Transform, Load (ETL) processes. Firstly, CDC provides near real-time data updates, while ETL processes typically involve periodic batch updates, often leading to data latency. This ensures instant data availability, thereby improving decision-making processes.
Secondly, CDC reduces the load on the source system as it only captures
and processes the changes, unlike ETL, which often involves full data loads. This enhances system performance and minimises the risk of data loss or corruption.
Thirdly, CDC ensures better data consistency and integrity across systems by capturing all data changes, while ETL might miss capturing some changes due to its periodic nature.
How CDC Improves Data Quality?
CDC plays a crucial role in improving data quality. By capturing and processing data changes as they occur, CDC ensures that the data in the target system is always up-to-date and accurate. This eliminates data staleness and inconsistencies, thereby enhancing data quality.
Moreover, CDC reduces the risk of data loss or corruption as it only deals with the changes rather than the full data load. This not only preserves the integrity of the data but also makes the data more reliable and trustworthy.
How CDC Helps in Data Integration?
CDC is a powerful tool for data integration. It enables seamless and efficient data transfer between systems, ensuring that all systems are updated with the latest and most relevant data. This improves data consistency across systems and eliminates data silos, enhancing accessibility and usability.
Also, CDC supports different data formats and platforms, making it a versatile solution for data integration. Whether the data resides in on-premise databases, cloud-based systems, or hybrid environments, CDC can capture and deliver changes to all these systems in an efficient and timely manner.
How CDC Is Advantageous Over Other Data Processing Techniques?
CDC offers several advantages over traditional data processing techniques. It provides near real-time data updates, ensuring maximum data relevancy and currency. It reduces the load on the source system, enhancing its performance.
It ensures data consistency and integrity across systems, improving data quality. It facilitates efficient data integration, eliminating data silos.
Last but not least, it supports various data formats and platforms, making it a versatile solution for data management. Given these advantages, it’s clear that CDC is a robust and powerful tool for modern data-driven businesses.
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