Change Data Capture with Sparkflows
Welcome to the world of efficient and real-time data synchronization with Sparkflows' Change Data Capture (CDC) solution. In today's fast-paced business landscape, staying up-to-date with the latest data changes is crucial for making informed decisions. Our CDC solution powered by Apache Spark simplifies this process, ensuring you never miss a beat when it comes to your data.
What is Change Data Capture?
Change Data Capture is a technique that identifies and captures changes made to the data in a database. These changes can include inserts, updates, and deletes. CDC helps organizations maintain synchronized data across various systems and enables timely actions based on the latest information.
Why Sparkflows CDC?
Change Data Capture is a technique that identifies and captures changes made to the data in a database. These changes can include inserts, updates, and deletes. CDC helps organizations maintain synchronized data across various systems and enables timely actions based on the latest information
Real-time Data Sync
With Sparkflows CDC, you can capture data changes in near real-time, allowing you to react quickly to changes and trends in your data
Efficient Data Processing
Leveraging the power of Apache Spark, Sparkflows ensures high-speed and parallelized processing of data changes, enabling rapid data synchronization
Ease of Use
Our intuitive interface allows you to configure CDC workflows without requiring extensive coding knowledge. You can set up and manage your CDC pipelines with ease
Flexible Integration
Sparkflows CDC seamlessly integrates with various data sources and sinks, including databases, data warehouses, cloud storage, and more. This flexibility ensures that you can use the tools and platforms you're already familiar with
Change Tracking
Gain a clear understanding of what data changes occurred, when they happened, and their impact on your systems. This comprehensive tracking aids in auditing and troubleshooting
Event-Driven Architecture
Sparkflows CDC operates on an event-driven architecture, ensuring that data changes trigger immediate actions, such as notifications or further data processing
Key Features
Automated Change Detection
Sparkflows automatically detects data changes, reducing the need for manual intervention and minimizing errors
Schema Evolution Handling
As your data evolves, Sparkflows CDC accommodates changes in the data schema, ensuring a smooth transition without disrupting operations
Data Transformation
Customize your data transformation and enrichment processes within the CDC pipeline, preparing your data for consumption in downstream systems
Data Consistency
Sparkflows CDC maintains data consistency across systems by ensuring that changes are accurately captured and applied
Two ways of CDC with Sparkflows
Sparkflows provides 2 ways for CDC
Log Based
Sparkflows listens to changes in the Tables and then applies the changes to the target system. This is a streaming solution
Query Based
Sparkflows queries the source table for latest updates and then applies the changes to the target system
