Efficient Change Data Capture for Agile Enterprises
Build CDC pipeline with Kestra for data synchronization, and ressources optimization to ensure your data analytics platforms reflect the most current information.Get started
What is Change Data Capture ?Change Data Capture (CDC) tracks and captures data source alterations for efficient processing and analysis. Kestra's CDC features allow businesses to synchronize and manage data effectively, maintaining updated data warehouses and analytics platforms with minimized resource use.
Streamlined Data Analysis for Financial Institutions
Simplify CDC with Kestra
Kestra makes CDC implementation straightforward, enabling your team to monitor data changes across your systems with ease. Configure workflows to automatically transform and aggregate data, keeping your datasets accurate and analytics reliable without manual intervention.
Real Time Data Replication
Streamlined Data Detection and Distribution: Kestra actively listens for changes in your chosen databases, such as PostgreSQL, detecting new entries and updates as they occur. Once identified, these changes are packaged and distributed to messaging systems like Apache Kafka, guaranteeing that downstream processes have access to the latest data without delay.
Data Detection and Distribution: Listen for changes in your chosen databases, such as PostgreSQL, detect new entries and updates as they occur. Once identified, these changes are packaged and distributed to messaging systems like Apache Kafka, guaranteeing that downstream processes, including analytics processing performed by tools like Apache Spark, have access to the latest data without delay.
Workflow Configuration and Management with Kestra
Kestra enables institutions to configure and manage CDC workflows with tasks such as data transformation, filtering, and aggregation, resulting in a consistent and up-to-date data view for accurate insights and decision-making.
Event-Driven Triggers for Automatic Workflow Initiation
Kestra's event-driven triggers initiate workflows automatically as data changes occur, eliminating manual intervention and reducing data inconsistency risks.
Automated Workflows : Kestra CDC workflows are fully automated, with the capability to initiate and manage workflows that respond to file detection or scheduled events, API calls, or webhooks. This automation extends to data processing, enabling immediate ingestion, transformation, and delivery to data warehouses and analytical platforms, maintaining the integrity and relevance of your data landscape.
Integration with Existing Infrastructure
Kestra's advanced integration capabilities enable connectivity across your entire stack and third-party applications, ensuring streamlined data synchronization throughout your entire ecosystem."
Kestra - Empowering Data-Driven Success
Kestra's Change Data Capture capabilities enhance data management by enabling efficient data synchronization and processing. With Kestra's event-driven triggers, visual pipeline editor, and extensive integrations, businesses can maintain up-to-date data across their systems, leading to timely insights and better decision-making. Experience the benefits of Kestra's CDC solution and transform your data management processes today.
Leveraging Kestra's Features
Efficient Data Capture
CDC allows you to track and capture data changes promptly, ensuring that your data pipelines remain current and provide you with accurate, actionable insights.
Integration with Data Sources
Integrates with various data sources, databases, and platforms, allowing you to track changes across your entire data ecosystem.
Kestra is designed to handle high data volumes and can scale to meet your growing data needs.
Reduced Data Latency
Reduce data latency, ensuring that your data pipelines and analytics are always based on the latest information.
Allow you to track and manage historical versions of your data for auditing, analysis, and compliance purposes.
Delayed Data Capture
Configure time delays for capturing data changes for handling time-sensitive data or for mitigating the impact of temporary data inconsistencies.