Unlock Data Movement with Change Data Capture (CDC): Explore the Best Implementation Techniques.
As data sets expand and the need for real-time responsiveness increases, traditional ETL processes face challenges in keeping pace. To address this, databases are now turning to change data capture (CDC) as a solution.
CDC enables the transfer of data in small, real-time increments rather than relying on bulk loads or batch windows. This capability empowers businesses to make quicker and more precise decisions with the advantage of real-time data movement.
In this concise guide, we will delve into the fundamentals of CDC and its advantages.
What is change data capture (CDC)?
Change Data Capture (CDC) is a technology and process used in the field of data management to capture and record changes made to databases and various data sources, including SaaS applications or systems accessible through APIs. It is commonly employed for data replication, facilitating consolidated access to operational data for analytics, data streaming, and machine learning applications.
The popularity of CDC is swiftly rising due to two main factors.
- Firstly, as a form of data replication, it drives powerful data-driven use cases, allowing organizations to leverage real-time data for critical decision-making processes. This is especially valuable for businesses operating around the clock without convenient batch windows, as it ensures data is transferred in reliable manner, enabling uninterrupted access to the most important information for driving key business processes and maintaining a competitive edge.
- Secondly, CDC's ability to capture data changes as they occur makes it indispensable for organizations that rely on 24/7 operation of critical systems. By providing continuous data transfer, CDC ensures that systems remain up-to-date and synchronized without requiring extensive manual intervention or time-consuming database reloads. This enhances data consistency and enables timely decision-making based on the latest information available.
As a result, CDC is increasingly becoming a go-to solution for data integration, replication, and analytics in today's fast-paced and data-centric business landscape.
ETL Vs Change Data Capture
ETL (Extract, Transform, Load) and Change Data Capture (CDC) are both crucial concepts in the field of data management, but they serve different purposes and play distinct roles in data integration and processing workflows.
ETL is commonly used in scenarios where data needs to be moved from multiple sources to a centralized location for analysis, reporting, and business intelligence purposes. However, traditional ETL processes often involve periodic batch processing.
CDC, on the other hand, is a technology and process designed to capture and track changes made to a database or data source. Instead of replicating the entire dataset, CDC captures only the changes (inserts, updates, deletes) made to the source data since the last extraction. This approach ensures that the target system is continuously updated with the latest changes, enabling near real-time data integration and synchronization.
Let's explore how Change Data Capture (CDC) is integrated at each stage of the ETL process.
Extract
In the extract stage, Change Data Capture (CDC) offers a continuous flow of change data.
In the conventional approach, this stage is executed in batches, where a single database query extracts a large volume of data in bulk. While effective initially, this method becomes inefficient when the source databases undergo frequent updates.
In such cases, the need to refresh a replica of the source tables each time may lead to inaccuracies in the target table, as it might not reflect the current state of the source application accurately. CDC solves this issue by maintaining a data stream, ensuring that changes are captured and reflected immediately, without relying on batch processes.
Transform
At the transform stage, CDC introduces novel efficiencies.
Traditionally, ETL tools require the transformation of entire data sets to align with the structure and format of the target table or repository before loading. While this aspect remains relevant with CDC, it deviates from transforming large batches of data all at once.
With CDC, data is loaded continuously as changes occur in the source, and the transformation is performed directly in the target data repository. This shift in approach becomes essential due to the ever-expanding size of contemporary data, making the process not only more efficient but also necessary to maintain pace with the data growth.
Load
As evident from the previous "transform" section, CDC facilitates the near-simultaneous occurrence of load and transform processes. In fact, in CDC, loading happens before transforming, mainly because many cloud-based target repositories (e.g., data warehouse, data lake, etc.) are equipped to handle the transformation internally.
Benefits of change data capture
Imagine having access to the latest data changes as they happen – that's the power of CDC. With real-time or near-real-time data capture, CDC ensures that your target systems are continuously updated, empowering you to make informed decisions without data latency.
Minimized Impact on Source Systems:
One of the great advantages of CDC is its ability to work independently of the source systems. Unlike traditional ETL processes, CDC captures changes without putting any strain on the source databases. This ensures smooth operations and uninterrupted workflows, even during data transfer.Efficient Data Integration:
Bid farewell to extensive data reloads and time-consuming transformations. CDC captures only the changes, streamlining data integration and synchronization processes. As a result, your target systems stay effortlessly synchronized with the latest updates from the source.Improved Data Accuracy:
Accuracy is the cornerstone of successful data management. CDC's real-time nature ensures that your target systems mirror the most current state of the source. Say goodbye to data inconsistencies and confidently rely on accurate information for your operations.Cost-Effectiveness:
Data storage and transfer costs can add up quickly, especially when dealing with massive datasets. CDC minimizes these costs by capturing and transferring only the changed data, optimizing network bandwidth and storage usage.Scalability and Versatility:
As your data needs grow, CDC grows with you. This versatile technology seamlessly handles data from various sources, supporting heterogeneous environments, and easily scaling to accommodate increasing data volumes.Enabling Real-time Analytics:
Making data-driven decisions requires access to the latest insights. CDC empowers real-time analytics, data warehousing, and business intelligence applications, giving your organization a competitive edge with faster insights and responses.
Change Data Capture with Datazip
At Datazip, we understand the importance of data integration and its impact on business decision-making. That's why our super data app is designed to provide an end-to-end data platform, offering everything from seamless data integration to interactive dashboards.
Datazip offers a comprehensive platform equipped with over 150+ pre-built connectors, empowering data teams to seamlessly centralize and transform data from numerous SaaS and on-premises data sources into cloud destinations. Our connectors are thoughtfully enhanced with CDC technology, guaranteeing efficient and high-volume data movement, thereby catering to a wide range of deployment options. With Datazip's cutting-edge technology, data integration becomes a hassle-free and efficient process, enabling organizations to unlock the true potential of their data.
Ready to experience the benefits of CDC? Sign up today to start your free trial in your own private cloud!
