Have you ever been asked to replicate data from an operational API into a warehouse, only to realize the API is, well, basic? No filtering by modification date, and you have to fetch all employees to get their IDs, then fetch each employee record by its ID. Yeah, it’s a real challenge.
I’ve been in this situation before, and I’m not alone. So, how do you handle replicating data in such cases?
## The Replication Logic
Do you fetch all employees and each detail record on every poll? Or do you maintain a record of all the raw data from each time you polled, then delete/merge/replace into the warehouse? These are important questions.
In my experience, it’s essential to add additional fields to the dataset, such as the time it was last fetched. This helps you keep track of changes and ensures data consistency.
## Polling vs. Manual Triggering
When the process becomes too loaded, do you still opt for polling? Or would you consider manually triggering the pipeline only when needed? I’ve found that a combination of both approaches can be effective. Polling can help with near real-time data replication, while manual triggering can reduce the load on the system.
## Best Practices
Here are some best practices I’ve learned:
– **Use incremental loading**: Load only the changes since the last fetch to reduce data volume and processing time.
– **Store raw data**: Keep a record of all raw data fetched from the API to ensure data consistency and auditing.
– **Add metadata**: Include metadata such as the time of fetch, data source, and any other relevant information.
– **Monitor and optimize**: Continuously monitor the replication process and optimize it as needed to ensure efficiency and data quality.
## Final Thought
Replicating data from operational APIs can be challenging, but with the right approach, it can be done efficiently. By following these best practices, you can ensure data consistency, reduce processing time, and make the most of your data.
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*Further reading: Data Replication Strategies*