DataOps Suite Release 2025.3.0.0 delivers major upgrades across ETL Validator, BI Validator, and Data Quality Monitor. ETL Validator adds PII data masking, an Excel Read File Component, and AI-powered mapping generation. BI Validator introduces Kubernetes-based stress testing (scaling from hundreds to thousands of users), Oracle Analytics stress testing, and Tableau Direct Trust authentication. Data Quality Monitor gains an Issue Management Dashboard, Snowflake-as-workschema support, and table-level ownership—helping teams move from detecting data issues to resolving them faster.
Key Takeaways
- ETL Validator adds security and AI automation — PII data masking protects sensitive data during processing, while AI-powered mapping generation analyzes SQL/stored procedures to auto-generate source-to-target mappings with transformations.
- BI Validator scales performance testing dramatically — Kubernetes-based stress testing expands simulated user load from hundreds to thousands of users, alongside new Oracle Analytics stress testing and frictionless Tableau Direct Trust authentication.
- Data Quality Monitor shifts from detection to resolution — a new Issue Management Dashboard tracks open, resolved, and ignored issues without requiring SQL, while table-level ownership improves accountability.
- Snowflake integration reduces data movement — bad records can now be stored and managed directly in Snowflake as a workschema, enabling BI reporting on flagged data without unnecessary duplication.
DataOps Suite Product Updates: Release 2025.3.0.0 — featuring PII masking, AI-powered automation, Kubernetes scale testing, and streamlined data quality workflows
Release v2025.3.0.0 introduces major enhancements across ETL Validator, BI Validator, and Data Quality Monitor—helping you secure sensitive data, validate BI performance at scale, and manage data quality issues with precision.
What’s New in DataOps Suite 3.0.0?
“With release 2025.3.0.0, Datagaps empowers teams to accelerate their data validation cycles while ensuring compliance sacalable performance.” – Datagaps Product Team.
ETL Validator Enhancements: Secure, Automated, Efficient
1. PII Data Masking
Protect sensitive data during ingestion, preview, and processing.
Benefit:
Ensures compliance with privacy regulations while enabling securecollaboration without exposing real data.
2. Excel Read File Component
Simplify Excel data ingestion.
Benefit:
Quickly select sheets or ranges, preview before processing, and reuse saved data to avoid repeated work.
3. AI-Powered Mapping Generation
Accelerate ETL testing with AI Agents.
Benefit:
Automatically analyze SQL or stored procedures, detect source-to-target relationships, and generate mappings with transformations.
Why ETL Validator Matters
ETL Validator now combines data security, automation, and efficiency—reducing manual effort, improving compliance, and speeding up validation cycles.
BI Validator: Scale Performance Testing
1. Stress Testing with Kubernetes
Simulate large-scale user sessions using Kubernetes clusters.
Benefit:
Scale beyond previous limits (from hundreds to thousands of users) and monitor performance in real time.
2. Oracle Analytics Stress Testing
Validate Oracle Analytics dashboards under peak load.
Benefit:
Detect bottlenecks early and ensure scalability for enterprise BI environments.
3. Tableau Direct Trust Authentication
Seamless Tableau access without repeated logins.
Benefit:
Faster, frictionless user experience with secure Direct Trust integration.
Why BI Validator Matters
BI Validator now enables enterprise-grade performance testing and frictionless authentication, ensuring your BI platforms deliver reliable insights under any load.
Data Quality Monitor: From Detection to Resolution
1. Issue Management Dashboard
Track and resolve data quality issues in real time—no SQL required.
Benefit:
Centralized dashboard to monitor open, resolved, and ignored issues with resolution timelines.
2. Snowflake as Workschema
Store and manage bad records directly in Snowflake.
Benefit:
Avoid unnecessary data movement and enable BI reporting on bad data.
3. Table-Level Ownership
Assign owners at the data model or table level.
Benefit:
Improves accountability and speeds up issue resolution with clear ownership.
Why Data Quality Monitor Matters
Data Quality Monitor now provides end-to-end visibility and accountability, helping teams move from detection to actionable remediation faster than ever.
Upcoming Features: What’s Next in DataOps Suite
These are the features that will be specific to individual product modules within the DataOps Suite.
BI Analyzer for Power BI
This is a diagnostic and optimization feature designed to analyze the internal structure and quality of Power BI Reports.
BI Catalog and Visual Analysis
This feature will allow users to select reports and map their associated dataflows for better tracking and usability.
HTML Rendering in Datagaps UI
The platform will support the ability to render the HTML content of any reports or profile reports generated using Python or Scala directly within the UI.
Excel Write Component
This feature will assist users by allowing them to write multiple data sets into Excel files in different locations.
Report Test Plan in Pipeline
This feature will provide a top-down approach for consolidated BI reports testing.
Watch the Latest Product Update Video
Watch the latest Datagaps DataOps Suite 3.0.0 product update to see the new features in action:
Explore DataOps Suite v3.0.0 and accelerate your data validation today!
Contact us today or explore the full release notes to see how Datagaps DataOps Suite can transform your data operations.
Frequently Asked Questions
1) What’s new in ETL Validator with the 2025.3.0.0 release?
ETL Validator introduces several enhancements, including PII data masking to protect sensitive information during testing, an Excel Read File Component for validating Excel-based data sources, and AI-powered mapping generation that automatically creates source-to-target mappings by analyzing SQL queries or stored procedures.
2) How does BI Validator’s Kubernetes-based stress testing improve performance testing?
BI Validator leverages Kubernetes to scale performance testing from hundreds to thousands of concurrent virtual users. This enables organizations to accurately evaluate how dashboards and reports perform under realistic peak workloads and identify performance bottlenecks before production deployment.
3) What is the new Issue Management Dashboard in Data Quality Monitor?
The Issue Management Dashboard provides a centralized interface for tracking data quality issues categorized as open, resolved, or ignored. Users can review and manage issues without writing SQL, making data quality monitoring more accessible for both technical and business teams.
4) How does Snowflake-as-workschema support help with data quality monitoring?
Snowflake-as-workschema support allows failed or flagged records to remain within Snowflake for analysis instead of being copied to another repository. This reduces unnecessary data movement while enabling BI reporting and investigation directly on the affected records.
Sushanth Kumar
Product Marketing Manager, Datagaps
Product Marketing Manager at Datagaps. Focused on the modern data ecosystem and how validation fits across ETL, BI, and analytics workflows.
Anand Rao Vala
VP Marketing, Datagaps
VP of Marketing at Datagaps. Go-to-market leader for enterprise data and analytics, with prior roles at Qlik, Informatica, IBM, and Hitachi Vantara.
