The Objective
The top healthcare company wanted to perform data validation during data warehouse migration to GCP. For the company, time was a major concern. They wanted to complete the data warehouse migration before the end of the life of IBM Netezza.
Customer initial plan involved a traditionally manual process for the data validation via the cloud engineers. This whole process was time-consuming and involved high business risk involvement if they exceeded the target timeline.
In addition, the manual method could be tedious, involve the risk of data losses, limited to performing only sample testing (not on the complete database). and wouldn’t be able to perform multiple instances of data validation.
The Challenges
25 BQ engineers would have been responsible for manual data validation. They would be performing only sample testing (not on the complete database). and they wouldn’t be able to perform multiple instances of data validation.
Solutions
Datametica used Pelican. our automated product for data validation and reconciliation. Pelican is a unique product to validate and reconcile Petabyte-scale of data at the cell level and across heterogeneous systems to automate, accelerate and simplify the data validation process during the data migration to the cloud.
Pelican helped the customer accomplish a reduction in time by running in parallel during the migration process. Pelican offers parallel validation capability whereas other competitors and tools offered to perform data validation only post-migration. This saved almost 60% of the time for customers compared to the manual validation process.
Key features of Pelican:
- Automation: Automated data validation & reconciliation
- Zero Factor: No coding requirement & data movement
- Accurate: Intelligent comparison at cell level
- Parallel run: Validation during migration
- Insights: Detailed reports identifying discrepancies
Benefits
By choosing to with Pelican, the client saved 60% of the time in the data validation process in comparison to the previous panned manual method.
The additional benefits of Pelican were the validation of the whole dataset till cell-level each and every time (when needed) instead of the traditional method of sampling and one time-validation. This ensured 100% accuracy. This also brought confidence in decommissioning the legacy system. Pelican did the work of tens of engineers that otherwise would be needed for completing the data validation.
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