Migrating to a cloud data warehouse introduces three critical validation challenges: ensuring 100% data accuracy during bulk migration, running old and new ETL processes in parallel without data loss, and confirming analytics/BI tools function correctly post-migration. Manual validation across truncations, precision mismatches, and referential integrity issues can derail timelines and erode trust in the new environment. Datagaps automates this entire validation lifecycle—bulk migration testing, incremental ETL testing, and analytics regression testing—cutting migration testing time and QA costs while ensuring source-to-target data integrity throughout the cloud transition.
Key Takeaways
- Modern Data Warehouse migration demands 100% data validation — format incompatibilities between on-premise and cloud structures are a leading cause of mistrust in migrated data.
- Old and new ETL processes must run in parallel and be continuously tested — switching data stores (Snowflake, Databricks, Redshift) often forces a shift in ETL tooling or a move to ELT.
- Analytics cutover requires multi-layer testing — regression, metadata, dashboard-to-dashboard, and performance/stress testing all need to pass before cutting over to the new environment.
- Automated testing shrinks migration risk and cost — Datagaps’ test case builder reduces migration testing time, data quality testing time, and QA costs versus manual validation.
We’ve decided on a new Cloud-based Data Warehouse. Now what?
Cloud data migration is a project where an organization moves its data warehouse, ETL processes, and analytics tools from on-premise infrastructure to a cloud platform such as Snowflake, Databricks, or Redshift — and validating that move correctly is the hard part. Datagaps has recently seen an uptick in data validation inquiries tied to Cloud Data Migration projects, driven by the undeniable success and cost savings organizations see from moving to the Cloud. Per Gartner’s Hype Cycle framework, we believe the market is well past the Trough of Disillusionment and well down the path of the Plateau of Productivity, though some argue the Trough was never fully experienced.
When you pass through the Slope of Enlightenment, the challenges are surfaced more clearly. As we are a data and BI validation company, our clients that are migrating to the cloud ask three things.
- How do I validate that the data is migrated correctly?
- How will this affect my ETL processes?
- How do I check that my analytics tools function correctly in the new environment?
Challenges in Migrating to the modern Data Stack
1. Implementing the Modern Data Warehouse. Moving to the Modern Data Warehouse typically takes the shape of fork lifting the current data in the on-premise Data Warehouse and migrating that data with minimal changes into the modern data stack. Often this movement of the data results in mistakes due to a variety of reasons, not the least of which is the data formats in the new warehouse aren’t completely compatible with the old. The new Data Warehouse and the old need to be validated by comparing 100% of the data elements in each. Not doing so can lead to mistrust and loss of credibility.
2. Incremental ETL Testing. The ETL processes that once feed the on-premise Data Warehouse incrementally now have to feed the cloud data warehouse generally in a different data store such as Snowflake, Databricks, or Redshift. The change in datastore typically drives a shift in the ETL tool and/or the incremental ETL processes. So now, there are two ETL processes. One is the old ETL process, and the other is what feeds the new Cloud Data Warehouse.
Often, a change is made to swap out the ETL tool to take advantage of some of the differences in the new modern data stacks. Sometimes, this takes the shape of the new process as an ELT process to transform the data once it is loaded. Again, these results need to be validated together to determine if the new techniques replicate all of the data transformations required to support the data analytics to follow.
3. Impact on Data Analytics. Finally, you need to point your analytics tools to the new Cloud platform. Often multiple data analytics platforms are involved as well. In some cases, companies take this opportunity to consolidate analytics platforms, and most don’t due to the enormous amount of additional development and testing required.
In each of these steps, data must be extensively validated.
Diagram showing on-premise to cloud data migration architecture — data sources, ETL workflow, data warehouse, and analytics moving to the cloud
| Migration Challenge | How Datagaps Addresses It |
|---|---|
| Implementing the Modern Data Warehouse — moving 100% of data elements from on-premise to Cloud without introducing format mismatches | Bulk Migration Testing: an automated data migration test case builder compares the new Cloud data structure against the older on-premise structure |
| Incremental ETL Testing — running old and new ETL/ELT processes in parallel to confirm the new process replicates all required data transformations | Incremental ETL Testing: old and new ETL processes are continuously tested in parallel, without extensive manual transformation work, to confirm they produce matching results |
| Impact on Data Analytics — repointing analytics platforms to the new Cloud environment and validating they still function correctly | Analytics Testing: regression testing, metadata testing, data validation between old and new systems, report-to-database query verification, GUI/dashboard pixel comparison, and performance and stress testing |
Automating the Data Migration Validation with Datagaps
1. Bulk Migration Testing. Bulk migration to the Cloud introduces truncation errors, precision mismatches, and broken referential integrity — fundamental data quality problems that creep into large-scale data moves. Datagaps’ data migration test case builder automatically creates source-to-target comparisons between the new Cloud data structure and the older on-premise structure. This approach shaves months off the data migration project timeline.
2. Incremental ETL Testing. In conjunction with getting the data moved successfully, most organizations run a parallel ETL replacement project to prepare the ETL processes to do the incremental loads in the new data warehouse. Whether the ETL tool is being replaced with newer technology or not, data validation testing is still an essential requirement. The old and new ETL processes run in parallel for a period of time and need to be continuously tested to ensure the processes are working correctly. Much like Datagaps provides data migration testing for the Bulk migration test, we can produce these test cases in an automated fashion without the need to do extensive transformations, which save time and cost significantly.
3. Analytics Testing. When the data analytics platforms are redirected to the new cloud implementation, several tests need to be performed in parallel.
- Regression testing between the new and the old platform.
- Metadata testing to make sure the schemas haven’t been changed inadvertently and to validate the ones that need to be changed have been.
- Data validation testing is needed between the old and the Cloud to ensure the data is correct in both instances.
- A report to database query in both systems is needed to verify the expected results.
- Finally, old pixel to new cloud app pixel and old dashboard data to new dashboard data comparisons at the GUI interface level should be performed to check the technologies are performing the same in each environment.
- Performance and stress testing in the new environment.
Benefits of Automating the Data Migration Testing
Reduction in Migration Testing Time by
Overall Reduction of TCO by
Reduction in Data Quality Testing Time by
Reduction in QA Cost by
Conclusion:
A cloud data migration is only as trustworthy as the validation behind it. Getting the data into Snowflake, Databricks, or Redshift is the easy part — proving that every record transferred accurately, that old and new ETL processes produce matching results, and that analytics dashboards return identical numbers post-cutover is where most projects lose time and stakeholder trust. Manual validation simply can’t scale to catch truncation errors, precision mismatches, or referential integrity issues across millions of records. Automating this validation lifecycle, as Datagaps does through bulk migration, incremental ETL, and analytics regression testing, is what turns a risky cutover into a confident one. If you’re planning a cloud migration, build validation in from day one — not as an afterthought.
Need Automated Testing for Data Validation?
Frequently Asked Questions: Cloud Data Migration Testing and DataOps Suite
1) What are the biggest challenges in migrating data to the cloud?
The biggest challenges are validating that 100% of data elements transfer correctly into the new Cloud Data Warehouse, keeping old and new ETL processes in sync during the transition, and confirming that analytics tools and dashboards produce identical results after cutover.
2) Why does ETL testing matter during cloud data migration?
Moving to a cloud data store like Snowflake, Databricks, or Redshift often requires swapping the ETL tool or shifting to an ELT process. Since old and new pipelines run in parallel during the transition, continuous testing is needed to confirm both produce matching, accurate results.
3) How is data validated after migrating to a cloud data warehouse?
Validation includes comparing the old and new data warehouses element-by-element, checking for truncation, precision, and referential integrity issues, and running regression, metadata, and performance tests against the connected analytics and BI platforms.
4) How can automated tools reduce cloud data migration risk?
Automated platforms like Datagaps generate migration source-to-target comparisons and ETL test cases without manual scripting, which shaves months off validation timelines while cutting migration testing time, data quality testing time, and overall QA cost.
Rajesh Kumar A
Digital Marketing Manager, Datagaps
Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.
S P S Murthy Akella
Director, Technology Strategy, Datagaps
Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.
