Data replication takes data from your source database, business application, API, file storage, etc., and copies it into your destination data warehouse or transactional database. After identifying the data you want to bring in, you need to determine how to replicate it for meeting your business needs.
Given that the data replication method you choose will impact your data, we support various replication methods to give you as much flexibility as possible. The table below contains a high-level look at each of Etlworks' Replication Methods and compares their pros and cons.
| Method | Pros: | Cons: |
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| Uses database redo [transaction] log to track changes in the source. |
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| Synchronous tracking mechanism, in which the changes information will be available directly once the DML change is committed |
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| Uses a designated field, typically a TIMESTAMP, to track changes in the source. |
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| Uses table(s) updated by the database triggers to track changes in the source. |
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| Polls CDC events from the Kafka topic(s) to track changes in the source. |
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| Always polls the entire dataset from the source. |
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