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These are the type of Change Data Capture Mechanisms
Change Data Capture Methods
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Log-based CDC
This is the most efficient way to implement CDC. Log-based Change Data Capture (CDC) allows you to react to data changes in near real-time without polling the database or altering the application. Changes are logged in real-time on a transaction log and then delivered to a Kafka topic for ingestion by other systems. This is made much easier using Kafka Connect's straightforward out-of-the-box connectivity.
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Query-based CDC
Here you query the data in the source to pick up changes. Unlike polling-based CDC approaches, we process the change logs published by the database. This is faster, and also ensures that even changes coming from a different application (e.g. via MongoDB's op log) are properly captured and propagated to the target system.
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Trigger-based CDC
You alter the source application to trigger the writing to a change table and then move it in this method. A trigger is a procedural code, stored in the database catalog. It “triggers” an action to occur whenever data is changed that affects the trigger. Triggers can be either row-level or statement-level. Row-level triggers are triggered once for each affected row, whereas statement-level triggers run only once for a SQL statement regardless of how many rows it affects.
Methodology
What Lyftrondata empowers data capture?
