Validating Data Across
Platforms Remains Problematic
Utilize our 200+ data connectors to connect to
any source or target and continuously validate your
data throughout the CI/CD DataOps process
Technical Challenge
Your project pulls massive amounts of data from various data stores. You need to verify that your data movement or ETL process has been performing correctly.
How do you verify your data?
The QuerySurge Solution
QuerySurge, the leading AI-powered data quality platform that continuously automates data validation across your entire ecosystem, provides over 200 data connectors.
- Data Warehouses
- Hadoop data lakes
- NoSQL stores
- Business Intelligence reports
- Flat files (tab and delimited)
- traditional databases
- XML
- SOAP or restful web service
- JSON files
- Excel
- Mainframes
- see the full list of 200+ data stores we support here ⇒
QuerySurge pulls data from the data sources and target data stores and compares every record set for an exact match or for a threshold, providing you with full data validation quickly.
FAQ: How to Test Across Platforms
- Why is testing across platforms so difficult?
- Why do cross-platform data issues happen?
- How do organizations validate data between different platforms?
- How do teams test across cloud, on-premises, and hybrid environments?
- Can one solution test across databases, warehouses, lakes, and BI tools?
- How do teams validate data during platform migrations?
- How do organizations test data pipelines that span multiple technologies?
- How do teams catch cross-platform defects before production?
- How does QuerySurge help organizations test across platforms?
- What ROI can organizations expect from cross-platform data testing?
Why is testing across platforms so difficult?
Testing across platforms is difficult because data moves through different systems, formats, architectures, and transformation layers.
Why do cross-platform data issues happen?
They happen when mappings, transformations, formats, business rules, or load processes behave differently between systems.
How do organizations validate data between different platforms?
They compare data values, aggregates, record counts, and business rules across the systems involved.
How do teams test across cloud, on-premises, and hybrid environments?
They need a validation solution that works across the full architecture, not just one environment.
Can one solution test across databases, warehouses, lakes, and BI tools?
Yes. Many solutions are designed to validate data across databases, data warehouses, data lakes, files, and BI environments, enabling teams to test the full data pipeline. QuerySurge, Tricentis, iCEDQ, RightData, and DataGaps all test across platforms.
How do teams validate data during platform migrations?
They need to confirm that the data was moved completely, transformed correctly, and preserved without integrity loss. Automating source-to-target validation during migration and modernization efforts greatly enhances this effort.
How do organizations test data pipelines that span multiple technologies?
They need validation that follows the data across each stage of movement and transformation. Automated tools help teams test multi-platform pipelines end-to-end rather than validate only isolated pieces.
How do teams catch cross-platform defects before production?
They shift validation earlier and run it continuously as data moves through delivery workflows. This helps detect issues before they affect reports, analytics, or downstream operations.
How does QuerySurge help organizations test across platforms?
QuerySurge helps organizations validate data across different systems, environments, and technologies from a centralized platform. It makes cross-platform testing more efficient, repeatable, and scalable.
What ROI can organizations expect from cross-platform data testing?
Organizations can reduce manual effort, catch integration defects earlier, improve confidence in data movement, and lower the risk of downstream errors. Automation helps make cross-platform validation faster and more dependable.
