In the Modern data landscape, real-time data management is not just a need, but a prerequisite for operational efficiency and informed decision-making. The Change Data Capture (CDC) technology has emerged as a revolution in achieving this, by tracking and capturing data modifications in real-time. As applications evolve, the requirement for diverse data models and access patterns escalates, propelling the need for a robust CDC mechanism. This article unveils the essence of CDC, delves into DataByte’s event-driven CDC capabilities, and highlights the transformation it brings to data operations.
Why is Change Data Capture (CDC) needed?
Initially, applications operate on a modest data footprint, with a single database catering to their data needs. However, as they evolve, the necessity for different data models, full-text search indexes, caching mechanisms, and comprehensive data analytics arises. This evolution transforms a simple architecture into a complex ecosystem with data residing in multiple places in a redundant and denormalized manner.
CDC stands as the bridge to synchronize the source and derived data systems. It meticulously observes all data changes in the source database, and extracts & replicates them to the derived data systems in real-time, maintaining data consistency across the application landscape.
The below picture represents order table DML operations getting replicated :
The CDC process has three stages.
- Change detection
- Change capture
- Change propagation
Types of CDC
Log-based Change Data Capture (CDC)
Mechanism
- Log-based CDC operates by monitoring the transaction logs of the source database.
- Every change (insert, update, or delete) in the database is recorded in the transaction log, which serves as the basis for capturing data changes.
- The CDC system scans these logs to identify and capture any changes, which are then propagated to the target systems.
Benefits
- Real-time Synchronization: Offers near real-time synchronization between source and target systems.
- Low Overhead: Unlike other methods, log-based CDC imposes minimal performance overhead on the source database.
- Comprehensive Capture: Captures every change without the risk of missing any data alterations.
Use-Cases
- Ideal for scenarios demanding real-time or near real-time data synchronization.
- Suitable for systems with high transaction rates where performance impact needs to be minimized.
- Best choice for Data synchronization between Microservices.
Query-based Change Data Capture (CDC)
Mechanism
- Query-based CDC operates by periodically polling the source database to identify changes.
- It often relies on timestamp columns or version columns to detect updated records since the last polling.
- The identified changes are then captured and propagated to the target systems.
Advantages:
- Simplicity: It’s relatively simpler to implement compared to log-based CDC.
- Compatibility: Often compatible with databases that do not expose transaction logs.
- Configurable Polling Intervals: Allows for configurable polling intervals to balance between timeliness and system load.
Use-Cases:
- Suitable for scenarios where real-time synchronization is not critical.
- Ideal for databases or systems where transaction logs are not accessible or exposed.
What DataByte offers?
- Intuitive User Interface (UI): DataByte offers a user-friendly interface that allows users to configure Change Data Capture (CDC) effortlessly. Even those with limited technical knowledge can navigate and set up the system with ease.
- Guaranteed Data Delivery:Ensuring the accuracy and timeliness of data is paramount. With DataByte, you can be assured that your data will be delivered without loss, maintaining the integrity of information throughout the process.
- Detailed Monitoring Capabilities: Stay informed about the operations of your CDC. DataByte provides comprehensive monitoring tools, giving users a real-time view of data flows, errors, and system health.
- Robust Governance Inbuilt:Governance is not an afterthought. DataByte comes equipped with inbuilt governance(SLA/monitoring/actions/rules/traceability) features ensuring data quality, security, and compliance standards are met at all times.
- Inbuilt Event-Driven Architecture:Respond to changes in real-time. With its event-driven architecture, DataByte enables immediate actions or notifications based on specific data changes, ensuring a responsive data ecosystem.
- Cloud-Native & Kubernetes-Based:Designed for the modern cloud era, DataByte is natively built for cloud environments and utilizes Kubernetes for orchestration. This ensures scalability, resilience, and optimal performance in cloud deployments.
- Flexible Message Transformation:Data doesn’t always fit the mold. DataByte provides tools for message transformation, allowing users to modify and adapt data structures as they move between systems, ensuring compatibility and optimal integration.
Conclusion:
DataByte stands out as a holistic solution that bridges the gap between traditional and modern data practices. With its robust feature set — from an intuitive UI to its cloud-native architecture — DataByte ensures that businesses are not just keeping up with the times, but are also poised to lead in their respective industries. Whether you’re looking to streamline data delivery, enhance monitoring, or ensure impeccable governance, DataByte is the comprehensive answer to all your data-centric needs
Featured Image by rawpixel.com on Freepik
