Change Data Capture with Sparkflows
Real-Time Change Data Capture Made Simple
Efficient and Real-Time 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 environment, keeping systems updated with the latest data is essential for timely and informed decision-making. Sparkflows Change Data Capture (CDC) solution, powered by Apache Spark, simplifies this process by enabling efficient and real-time data synchronization across platforms.
What is Change Data Capture?
Change Data Capture (CDC) is a technique used to identify and capture changes made to data in a database—such as inserts, updates, and deletes. It helps organizations maintain synchronized data across systems and ensures timely action based on the most current information.
Sparkflows CDC captures only new or modified records since the last extraction, avoiding full data reloads and enhancing performance. Additionally, extraction history and metadata are automatically stored, offering complete traceability and auditability.
Key Features
Automated Change Detection
Sparkflows automatically detects data changes, reducing the need for manual intervention and minimizing errors
Schema Evolution Handling
Sparkflows CDC adapts to evolving data structures, managing schema changes gracefully without interrupting business operations.
Data Transformation
Users can enrich and transform data directly within CDC pipelines, preparing it for seamless use in downstream systems.
Data Consistency
Sparkflows ensures consistency across all systems by accurately capturing and replicating data changes end-to-end.
Why Sparkflows CDC?
Sparkflows CDC plays a crucial role in helping businesses keep their data ecosystems aligned with real-time changes. It supports a wide range of source systems, including traditional databases, SaaS platforms, and API-based services.
CDC is fully compatible with platforms like Salesforce, NetSuite, Workday, and other SaaS or API-driven sources, enabling seamless integration and data synchronization.
Real-time Data Sync
Sparkflows CDC captures data changes in near real time, allowing businesses to respond quickly to shifting data trends and events.
Efficient Data Processing
Sparkflows uses Apache Spark for fast, parallel processing of data changes and supports both real-time and batch-based CDC for diverse sources, including APIs and SaaS platforms.
Ease of Use
Sparkflows provides an intuitive, low-code interface that enables users to configure CDC workflows without deep technical expertise. CDC pipelines can be set up and managed with minimal effort.
Flexible Integration
The CDC solution integrates seamlessly with a wide variety of data sources and targets—including databases, data warehouses, cloud storage, and more—allowing organizations to work within their preferred ecosystems.
Change Tracking
Sparkflows CDC provides clear visibility into what changed, when it changed, and its impact—making auditing and troubleshooting easier.
Event-Driven Architecture
Sparkflows CDC operates on an event-driven architecture, allowing data changes to trigger actions such as alerts, downstream workflows, or automated transformations.
How Sparkflows CDC Works?
The Sparkflows CDC Engine connects to various sources—databases (like Oracle, MySQL), applications (Salesforce, SAP), and document stores (Google Drive, SharePoint, S3)—to capture only the changed data using logs, timestamps, indexes, or metadata.
It supports both real-time and batch ingestion, with features like incremental reads, version control, and data lineage. Users can design CDC pipelines with a low-code interface and send processed data to cloud lakes and warehouses like Snowflake, Databricks, and Redshift for immediate use in BI tools.
Methods of Implementing CDC
Sparkflows provides two powerful ways for implementing Change Data Capture.
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