Data Ingestion
Change Data Capture (CDC) Event Hub
Change data capture is a design pattern that automatically synchronizes changes from the source and triggers data ingestion. It is an essential component of any data lake ingestion pipeline, enabling source systems to populate data lakes immediately when a change occurs, rather than waiting for long-running batch uploads.How to implement Change Data Capture?
As C&F, we use different implementations depending on the source systems. We rely on audit columns, table deltas, triggers, or logs. We use two different methods to synchronize changes: push and pull. Either the source database pushes updates to downstream services and applications, or the downstream services and applications check the source database at regular intervals to retrieve updated data. Choosing the right tool and strategy for the available data sources and target data lakes is critical. There are several CDC tools available, some specialized for database CDC and others for external systems such as SAP. Depending on the organization's data sources and project constraints, we select the appropriate tools or, in some cases, build a custom CDC system.
Real time data availability
Update your data lakes in real time for instant analysis and decision making.
Reduced latency
Minimize the time between data generation and availability in the data lake.
Data consistency
Ensure consistency of changed data in the data lake with source systems to support accurate analysis.
Historical data tracking
Capture historical changes, providing a detailed audit trail and time-series analysis.
At C&F, we excel at data lake projects using Change Data Capture (CDC) solutions to synchronize data from disparate sources in real time. CDC efficiently handles large volumes of data, ensuring fast and accurate ingestion while minimizing system load. Although commercial CDC tools can be costly, we evaluate their benefits for each new customer to make informed decisions for data lake projects.
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