Change data capture (CDC) refers to the tracking of all changes in a data source (databases, data warehouses, etc.) so they can be captured in destination systems. In this guide, we will introduce the fundamentals of CDC, its benefits, and a few use cases.
Change Data Capture (CDC) is a method used in databases and data integration to track data modifications. It captures changes like inserts, updates, deletes, and Data Definition Language (DDL) changes in the source database, ideal for data replication, warehousing, and real-time analytics. CDC targets and captures modified data, reducing the need to scan entire databases, thereby minimizing workload and processing overhead.
In this article, we will guide everyone through why we need CDC, how CDC works, as well as its use cases, among other things. This will provide everyone with a comprehensive understanding of CDC.
Change Data Capture (CDC) plays an important role in ensuring data consistency across different data platforms. It simplifies data synchronization, supports real-time analytics, supports data warehousing projects, and ensures applications are always working with the most current information—no heavy manual work needed.
Here are some key advantages of using CDC:
In conclusion, Change Data Capture significantly enhances data management, aids in effective data integration across systems, and helps organizations operate with the most current and accurate information, which leads to improved decision-making and operational efficiency.
Several methods exist for implementing a change data capture (CDC) system. Earlier techniques like table differencing, change-value selection, and database triggers were replaced by log-based CDC features due to their efficiency and minimal performance impact. This method utilizes transaction logs to record database changes. This article discusses these three main approaches.
Databases have transaction logs that record all events, enabling recovery from crashes. Log-based change data capture reads new transactions from these logs, capturing changes without impacting application-level changes or scanning operational tables, thus preserving source system performance.
Advantages of this approach include:
However, this approach also presents challenges:
One method is using database triggers to create a change log in shadow tables. Triggers, which operate at SQL level, fire before or after INSERT, UPDATE, or DELETE commands. Some databases offer native trigger support. However, they require greater overhead, impact performance, and require maintenance for each table in the source database.
Advantages of this approach:
Disadvantage of this approach:
A good number of application users are hesitant to potentially change application functionality by integrating triggers into operational tables. It's essential for DBAs and data engineers to conduct comprehensive performance tests on any new triggers in their environment and evaluate if they're prepared to manage the additional workload.
Change Data Capture has many use cases. Here are a few examples.
Data change management often involves batch-based data replication. However, as real-time, streaming data analytics become more in demand, it's less feasible to go offline and copy an entire database for data management. CDC allows for continuous replication on smaller datasets and only addresses incremental changes.
Consider an online system that constantly updates your application database. CDC can capture incremental changes to the record and schema drift. So, when a customer updates their information, CDC updates the corresponding record in the target database in real time. In a consumer application, this means you can process and act on changes more quickly. Processing a hundred records takes less time than a million rows. With CDC, you can also decide how to handle the changes (e.g., whether to replicate or ignore them).
As organizations transition from monolithic to microservices architectures, they need to transfer data from source databases and potentially direct it to more than one destination system. CDC can help keep both source and target data stores synchronized during this process.
Data-driven organizations prioritize customer experience to retain and grow their client base. A prime example is the financial sector. If a large bank experiences a sudden spike in fraudulent activities, they need real-time analytics to alert customers about potential fraud. Transactional data needs to be ingested from the database in real time. CDC, combined with ML fraud detection, can identify and capture potentially fraudulent transactions in real time. It can then transform and enrich the data so the fraud monitoring tool can proactively send alerts to customers, who can then take immediate remedial action.
Increasingly, organizations are migrating to the cloud to reduce Total Cost of Ownership (TCO) and improve agility and elasticity. By using cloud-native services, companies can focus on creating new digital experiences instead of spending time and resources on configuring, maintaining, and managing their databases and infrastructure.
CONCLUSION
With the growth of data sets and the increasing need for timely applications, traditional ETL is having a hard time keeping up. So, what’s the solution for databases to meet these real-time requirements?
Enter change data capture (CDC). Instead of moving data in large batches or bulk loads, CDC moves data in small, real-time increments. This approach allows for quicker, more precise decision-making based on real-time data movement.
In this guide, we introduced the fundamentals of CDC, its benefits, and a few use cases.
In this article, we'll show you how to set up a continuous data pipeline that seamlessly captures changes from your Postgres database using Change Data Capture (CDC) and streams them to Apache Iceberg.
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In this article, we’ve demonstrated how to build a core fraud detection system using RisingWave. With minimal setup, you can easily integrate these components into your existing technical stack and have a functional fraud detection solution up and running.
