Can GCP NetSuite integration be used for large scale transfer every night (populating data warehouse use case)? Does it support one time load vs incremental load? My concern is that synching Transaction Line tables may take a long time if it loads entire table vs modified/ created records. Does it track deletion? Thank you as always for your answers.
Integrating NetSuite with GCP for large-scale data transfers, such as populating a data warehouse, is a common and feasible use case. GCP supports integration through tools like BigQuery, Dataflow, and third-party ETL solutions such as Fivetran, Stitch, or Hevo. These tools enable both one-time full table loads and incremental data loads. Incremental sync is especially important for high-volume tables like the Transaction Line table to avoid transferring the entire dataset nightly. Incremental loads can be managed using NetSuite's Saved Searches or APIs, filtering records by fields like LastModifiedDate. However, NetSuite does not natively track deletions in its tables. To handle this, you can use strategies like soft-delete flags or compare source and target datasets periodically to identify deletions. Tools like Fivetran offer features for automated incremental loads and schema evolution, but deletion tracking may require custom logic or occasional full refreshes for consistency. For optimized performance, implement batching, pagination, and schedule transfers during off-peak hours. This approach ensures efficient synchronization while minimizing transfer times and resource usage.
Thank you so much for prompt response. This gives me a pretty good idea.
Hi @anil789 yes, integrating GCP with NetSuite can support large-scale nightly data transfers for data warehousing, but the efficiency largely depends on how the integration is structured.
Key Considerations:
Full Load vs. Incremental Load
Handling Deletions
Alternative Approach
If you're looking for a scalable and automated solution, Windsor.ai provides NetSuite connectors that sync data to BigQuery while optimizing for incremental updates. This helps reduce unnecessary processing and improves data freshness.
Hope this helps!