Change Data Capture
In the fast-paced world of data management, staying ahead of the curve is not an option, it’s a necessity. Change Data Capture (CDC) is the secret weapon that allows businesses to keep pace with the constant flux of data.
In this blog, we will delve into the world of CDC, explore different approaches to implementing it, provide real-world examples, and understand why CDC is pivotal for modern data management. Furthermore, we will unveil how IOblend automates this crucial process, making it effortless and efficient for organizations.
Understanding Change Data Capture (CDC)
Change Data Capture is the technique of identifying and capturing changes in a database or data source. It allows organizations to track every modification, whether it’s a new record, an update, or a deletion, and transform these changes into a digestible format. CDC ensures that decision-makers have access to the most recent and accurate data, enabling data-driven decisions in real-time.
Various Approaches to Implement CDC
There are several approaches to implementing CDC, each suited for different use cases and infrastructures:
Trigger-Based CDC: This method uses database triggers to capture changes as they occur. When an event (insert, update, delete) happens, the trigger captures and logs the change.
Log-Based CDC: In this approach, CDC relies on the transaction logs of the source database. It reads the log files and identifies changes, making it highly efficient and minimally intrusive.
Query-Based CDC: Query-based CDC periodically scans the source database to identify changes. It’s a flexible approach but can be resource intensive if not optimised for performance.
Hybrid CDC: Combining elements of the above approaches, hybrid CDC offers a balanced solution tailored to specific use cases.
Examples of CDC in Action
E-commerce Inventory Management: Imagine an e-commerce platform that needs to keep track of product availability. With CDC, any change in inventory, such as a new product being added or an existing one going out of stock, is instantly captured. This ensures that customers see up-to-the-minute product availability.
Financial Services: In the finance sector, stock market data changes in real-time. CDC helps financial institutions capture and analyse these changes instantly, allowing traders to make informed decisions on the spot.
Healthcare: CDC plays a crucial role in healthcare, where patient records are continuously updated. Medical professionals can promptly access the latest patient data, such as test results and treatment history.
Why CDC is Essential for Data Management
Change Data Capture is a crucial component of modern architectures and offers several key advantages for data management:
Real-time Decision-Making: CDC provides access to the most current data, enabling organizations to make real-time decisions, which is critical in fast-moving industries.
Data Accuracy: By capturing changes as they occur, CDC reduces the risk of data inconsistencies and inaccuracies.
Efficiency: CDC minimizes the need for resource-intensive batch processing, significantly reducing processing times.
Compliance: In regulated industries like finance and healthcare, CDC ensures data compliance by capturing every change made to sensitive information.
CDC allows transactional data to be available in real-time, without putting stress on the source systems. CDC does not require changes in the source application and reduces the transferred amount of data to a minimum, enhancing data management efficiency.
Change data capture makes it possible to replicate data from source applications to any destination without the burden of extracting or replicating entire datasets.
IOblend: Automating Change Data Capture
IOblend is an end-to-end enterprise data integration solution that incorporates all core DataOps capabilities. Here’s how IOblend streamlines CDC:
Automated Hybrid CDC: IOblend automatically captures changes in your data sources, eliminating the need for manual monitoring. If your system supports log-based CDC, IOblend will track all changes (inserts, updates and deletes) and materialise the full history and/or the latest updates as required. If you are using trigger-based CDC, we will query against the triggers. Finally, we use a highly optimised query-based CDC, a proprietary algorithm to scan for changes between “created” and “modified” dates for each record on reads, placing only minimal stress on the source systems/databases. All three methods require no coding. We give you the full flexibility to deploy any type of CDC that suits your architecture.
Seamless Integration: Perform CDC on any source, perform in-memory transformations, and sink the results to any destination with minimal effort. We combine CDC with advanced ETL capabilities to greatly enrich your data management capabilities without the requirement to code.
No Need for Staging: IOblend’s in-flight ETL capability reduces processing times by performing transformations on the new and updated data without the need for staging. Your data never leaves your security umbrella unlike with most SAAS solutions.
Full DataOps: IOblend covers the entire data journey, from record-level lineage tracking to metadata management, schema evolution, event handling, and much more.
Change Data Capture is a game-changer in the world of data management, and IOblend takes it to the next level with its automation capabilities. With IOblend, you can effortlessly capture changes in your data, reduce processing times, ensure data accuracy, and empower your organization with real-time insights. There is no need to use any additional tools or third-party modules with IOblend – we provide the full “end-to-end” data integration capability out of the box. Embrace the power of CDC with IOblend and stay ahead in the data-driven race.
Download your FREE Developer Edition now and experience the future of data management.
Managing Change Data Capture (CDC) effectively, a crucial component in modern data management, is simplified by IOblend’s automated approach. CDC, the process of identifying and capturing changes in databases or data sources, is pivotal for enabling real-time, data-driven decision-making. IOblend streamlines this with various CDC methods such as trigger-based, log-based, and query-based, suited for different use cases. This automated hybrid CDC ensures real-time data availability, accuracy, and minimizes the need for resource-intensive batch processing. IOblend’s capabilities also extend to seamless integration, in-flight ETL without staging, and full DataOps coverage, including record-level lineage tracking and metadata management. This comprehensive approach makes CDC an efficient and effortless process, allowing businesses to stay agile and informed with up-to-date data insights.
Real-Time Risk Modelling with Legacy & Modern Data
Risk Modelling in Real-time: Integrating Legacy Oracle/HP Underwriting Data with Modern External Datasets 💼 Did you know that in the time it takes to brew a cup of tea, a real-time risk model could have processed enough data to flag over 60 million potential fraudulent insurance claims? The Real-Time Risk Modelling Imperative Real-time risk modelling is
Unify Clinical & Financial Data to Cut Readmissions
Clinical-Financial Synergy: The Seamless Integration of Clinical and Financial Data to Minimise Readmissions 🚑 Did You Know? Unnecessary hospital readmissions within 30 days represent a colossal financial burden, often reflecting suboptimal transitional care. Clinical-Financial Synergy: The Seamless Integration of Clinical and Financial Data to Minimise Readmissions The Convergence of Clinical and Financial Data The convergence of clinical and financial
Agentic Pipelines and Real-Time Data with Guardrails
The New Era of ETL: Agentic Pipelines and Real-Time Data with Guardrails For years, ETL meant one thing — moving and transforming data in predictable, scheduled batches, often using a multitude of complementary tools. It was practical, reliable, and familiar. But in 2025, well, that’s no longer enough. Let’s have a look at the shift
Real-Time Insurance Claims with CDC and Spark
From Batch to Real-Time: Accelerating Insurance Claims Processing with CDC and Spark 💼 Did you know? In the insurance sector, the move from overnight batch processing to real-time stream processing has been shown to reduce the average claims settlement time from several days to under an hour in highly automated systems. Real-Time Data and Insurance
Agentic AI: The New Standard for ETL Governance
Autonomous Finance: Agentic AI as the New Standard for ETL Governance and Resilience 📌 Did You Know? Autonomous data quality agents deployed by leading financial institutions have been shown to proactively detect and correct up to 95% of critical data quality issues. The Agentic AI Concept Agentic Artificial Intelligence (AI) represents the progression beyond simple prompt-and-response
IOblend: Simplifying Feature Stores for Modern MLOps
IOblend: Simplifying Feature Stores for Modern MLOps Feature stores emerged to solve a real challenge in machine learning: managing features across models, maintaining consistency between training and inference, and ensuring proper governance. To meet this need, many solutions introduced new infrastructure layers—Redis, DynamoDB, Feast-style APIs, and others. While these tools provided powerful capabilities, they also
