Introduction to Change Data Capture (CDC)

In today's data-driven world, staying ahead means having quick and easy access to the freshest, most up-to-date information. As businesses generate and consume vast amounts of data every second, the need for real-time data integration and analysis has never been more crucial. This is where Change Data Capture (CDC) comes in. But what exactly is CDC, and why should you care?  In this article, we will discuss what CDC is, why it matters, and go through use case examples. Finally, we will compare options for CDC implementation. Let's dive in and explore this game-changing technology that's revolutionizing how businesses handle their data.

What is CDC?

Change Data Capture (CDC) is a process that tracks and identifies changes in data sources such as databases and data warehouses in real-time. It captures modifications like inserts, updates, and deletions as they occur, allowing organizations to react swiftly to critical events. This enables highly responsive applications powered by transactional systems.

Imagine you're running a busy e-commerce platform. Every second, customers are placing orders, updating their profiles, and interacting with your website or app. CDC captures each of these changes as they happen, allowing your applications and backend systems—from inventory management to customer analytics—to react swiftly and stay in sync.

But CDC isn't just about capturing changes. It is crucial for maintaining data consistency across multiple systems and environments. It allows organizations to efficiently propagate changes from source databases to various downstream systems for applications like caches, audit logs, full-text search, continuous queries, windowed queries, microservices data exchange, analytics platforms and many more.

Let's break it down with a real-world example. Imagine an e-commerce company that needs to perform analytics on their orders database. They can't afford to overload their production system with complex analytical queries. To ensure the integrity and performance of the production environment, they need to isolate this activity. Enter CDC. Here's how it helps when replicating a database:

1. It continuously captures changes in the source database.
2. It efficiently transfers only the modified data to a replica.
3. It keeps the analytical database up-to-date in near real time.

This approach is brilliant because it isolates the data for analytics. The company can optimize their analytical processes without impacting the source system. Plus, CDC ensures data freshness, enabling accurate, timely insights for business decision-making.

Why do you need CDC?

Continuous access and high availability of systems and data are paramount, especially in sectors like financial services and e-commerce. Traditional batch-based replication methods, while once sufficient, now struggle to meet the demands of modern enterprises. These methods are often slow, create network traffic spikes, and can lead to data inconsistencies, compromising the integrity of critical business operations.

Enter real-time data streaming analytics, a powerful approach that enables organizations to extract valuable insights from the vast amounts of data they generate and consume. However, without CDC, even streaming systems can lag, potentially leading to severe consequences in real-time applications.

Consider a security context where a lagging system could result in delayed threat detection, creating a vulnerable window for potential breaches. Or in operational scenarios, where metrics produced in batches rather than real-time can cause information downtime, leading to delayed decision-making and missed opportunities.

CDC addresses these challenges by enabling up-to-date and efficient replication. It facilitates the continuous, real-time capture and transfer of data changes, ensuring all systems remain synchronized with minimal delay. This capability is crucial for maintaining failover replicas, fully synchronized copies of primary databases or systems that can quickly take over operations if the primary fails, thus minimizing downtime and ensuring business continuity.

Moreover, CDC empowers organizations to maintain replicas for isolated testing. Development teams can leverage these replicas to test new logic or feature enhancements without risking the integrity of production systems, fostering innovation while maintaining stability.

Lastly, CDC forms the foundation for streaming applications. It provides reliable and consistent data streams that developers can use as input for their applications and data processing pipelines. These streams serve as building blocks for creating sophisticated real-time applications and analytics systems, enabling businesses to stay agile and responsive in an increasingly dynamic market environment.

Use cases for CDC

CDC has a vast potential for various applications in modern data architectures. The possibilities are so extensive that it's challenging to provide a complete list, with new use cases continually emerging. To give an idea of CDC's versatility, let's explore some sample use cases.

Application caches

Application caches are crucial for improving performance and reducing load on backend systems by storing frequently accessed data in memory. For example, an e-commerce site might cache product information to avoid repeated database queries.

These caches store read-only data to improve application performance. However, keeping these caches up-to-date can be challenging. CDC systems excel in this scenario. A cache updater captures the changes occurring in the source database and updates the cache directly, without needing to re-query the database and perform complex joins to reconstruct the cached data. For instance, if a product's price changes, CDC can update just that field in the cache, rather than rebuilding the entire product entry.

This approach ensures that the cache always contains the most current data, enhancing application responsiveness and data consistency.

Audit logs

In enterprise applications, retaining an audit log of your data is a common requirement. By capturing database transaction logs, CDC records every insert, update, and delete operation. However, raw CDC events lack crucial context like user identities, client details, and business process information.

Stream processing tools like Apache Flink can then enrich CDC streams with this vital metadata. By intercepting the CDC stream, we can correlate database changes with API calls, user sessions, and other contextual information.

Continuous queries

Continuous queries are an important tool for real-time data processing. They operate constantly, processing new data as it arrives. Imagine a bustling e-commerce platform where sales data is constantly flowing in. A continuous query calculating the running total of sales for each product category could power an incrementally updated materialized view. As new transactions are recorded, the view updates in real-time, providing instant insights into sales performance across different product lines without the need for batch processing or manual updates.

One key advantage of continuous queries is eliminating the need for polling or manual updates. This enables data-driven applications to react quickly to changes. And these queries have applications beyond basic calculations. They can be used to enable real-time machine learning inference on the latest data, keep analytics dashboards current, and power evolving pattern recognition systems.

For time-sensitive industries like financial trading and cybersecurity, continuous queries can provide a significant competitive advantage by enabling faster responses to new data.

These use cases represent just a fraction of CDC's potential applications. For a more comprehensive exploration of CDC use cases, including real-time ETL, microservices data exchange, and more, check out Seven Ways to Put CDC to Work.

Should you build or buy CDC?

When it comes to implementing CDC, organizations often find themselves at a crossroads: build a custom solution or opt for an off-the-shelf product or service? Both paths have their merits and pitfalls, and the right choice depends on your specific needs and resources. Let's break it down.

The DIY approach: Building your own CDC

Building your own CDC system can be appealing for several reasons:

Tailored fit: You get to design a system that fits your exact requirements. This can be beneficial for organizations with exactingly unique needs. For instance, a fintech company might need a CDC solution that integrates seamlessly with their proprietary risk management systems and adheres to specific regulatory requirements and internal policies.

Full control: With a custom-built solution, you have complete access to every component. This level of control can be desired by organizations with deep internal expertise who are dealing with sensitive data, like healthcare providers handling patient information.

No licensing fees: By building in-house, you avoid ongoing licensing fees, which can be intimidating when starting a project.

However, the DIY route isn't without its challenges:

Risk of unexpected expansion: As your needs evolve, you'll need to adapt your system. This ongoing maintenance and development requirement can be resource-intensive.

All responsibilities are on you: When building a production solution, you're in charge of everything from ensuring rock-solid security to maintaining system stability. When issues crop up (as they inevitably will), you'll need to tackle them head-on without the safety net of external support. It's a big responsibility.

Time and expertise requirement: Building and maintaining a CDC system requires substantial expertise across a range of systems that need to be integrated—a significant investment in time and resources. 

The Ready-made route: Buying a CDC solution

Opting for a commercial CDC solution has its own set of advantages:

Scalability on demand: Most modern CDC platforms offer cloud-based services that can grow with your needs. This flexibility is crucial as your data volumes expand.

Support and security: Reputable providers offer robust support and security measures, giving you peace of mind.

Quick time-to-value: One of the biggest advantages of buying a CDC solution is the speed of implementation. These platforms abstract away much of the underlying complexity, enabling you to harness the power of CDC without needing to become an expert in the underlying details. It's like getting a high-performance car without having to learn how to build the engine yourself.

But there are potential drawbacks to consider:

Vendor lock-in: You're tying your CDC capabilities to a specific provider. What if their roadmap diverges from your needs? Choosing the right partner can make all the difference.

Potential limitations: Some solutions might not support all your use cases or integrate smoothly with your existing workflows.

The Decodable approach to CDC

When it comes to implementing CDC, Decodable takes a refreshingly straightforward approach. We've designed our platform with ease-of-use in mind, stripping away the complexities that often accompany CDC setup and maintenance.

With Decodable, getting CDC streams up and running is as simple as connecting to your database using one of our fully-managed connectors. Our platform automatically creates these streams, eliminating the need for intricate configurations and reducing the potential for errors. It's CDC made easy, plain and simple.

We've simplified the CDC implementation process to just three steps:

1. Configure the source connector by accessing your database with Decodable.
2. Optionally build your business logic using SQL, Java, or Python for processing the CDC stream.
3. Create a sink connector to bring the processed stream into your target system.

This streamlined approach allows you to quickly implement CDC without getting bogged down in technical intricacies.

But we didn't stop at simplification. Decodable also provides extensive support for a wide range of tools and technologies. We offer the full power of Apache Flink to support processing CDC streams, allowing you to leverage Flink's robust stream processing capabilities. Our platform integrates seamlessly with all major stream connectors, ensuring compatibility with your existing data infrastructure. And we support multiple programming languages and APIs, including Python, Java, and SQL, giving you the flexibility to work in your preferred environment.

Conclusion

CDC is a powerful tool that supports a wide array of use cases across organizations of all sizes and industries. By capturing and streaming data changes in real-time, CDC enables companies to build responsive, event-driven applications and analytics systems that can react instantly to new information.

Implementing CDC within your data architecture unlocks the full potential of real-time data processing, dramatically reducing lag times in mission-critical systems. This allows businesses to make faster, more informed decisions based on the most up-to-date information available.

While CDC offers immense benefits, setting up and managing these systems has traditionally been complex. That's where solutions like Decodable come in, simplifying the entire process of implementing and administering secure CDC pipelines.

Don't let your business fall behind in the fast-paced world of real-time data. Experience the transformative power of CDC for yourself.