The Spring 2025 platform release for Datagaps DataOps Suite focuses on speed, visibility, and control at the infrastructure level rather than individual product features. Key additions include native support for Microsoft Fabric alongside existing SQL Server and Synapse integrations, and full DataOps Suite integration of Oracle Analytics and Publisher support that was previously exclusive to the thick client version — bringing consistent, automated validation to enterprise-scale Microsoft and Oracle BI environments.
Key Takeaways
- This release targets platform infrastructure, not individual features — it’s the “advancements” companion to a separate “new features” post covering ETL Validator, BI Validator, and Data Quality Monitoring.
- Native Microsoft Fabric support was added — complementing existing SQL Server and Synapse integrations for end-to-end testing across the Microsoft data stack.
- Oracle Analytics and Publisher support moved into the core DataOps Suite — this capability was previously exclusive to the thick client version.
- The release goal is enterprise-scale consistency — bringing automation and validation reliability to BI environments that previously required separate tooling.
1. Expanded Data Source Support
Microsoft Fabric Joins the Fold
The DataOps Suite now natively supports Microsoft Fabric, complementing our existing integrations with SQL Server and Synapse. This enables seamless end-to-end testing across modern data platforms, significantly reducing validation time and boosting confidence in analytics built on the Microsoft stack.
Oracle Analytics and Publisher Integration
In parallel, we support Oracle Analytics and Publisher. This support, which was exclusive to our thick client version, is now fully integrated into the DataOps Suite. This expands validation capabilities and brings the power of DataOps to enterprise-scale BI environments with enhanced consistency and automation.
2. Smarter Dataflows Management
Bulk Import of Dataflows
Migrating test cases across environments just got faster. Users can now import entire sets of dataflows in one go, eliminating the need for CLI tools or tedious one-by-one uploads.
Bulk Edit Functionality
Whether you’re changing owners, tags, or descriptions, our new bulk edit feature streamlines batch modifications, allowing for efficient ownership updates, tagging, and metadata adjustments all in a single, streamlined process.
3. Engine Enhancements for Modern Workloads
Centralized Kubernetes Management
Users now get a unified view of all Kubernetes-based engines, with easier start/stop controls, session visibility, and resource allocation all in one place.
Multiple Engine Creation for Pipeline Tasks
The Platform now lets you launch multiple Kubernetes pods for a single pipeline to enable faster concurrent task execution and better fault isolation, which is perfect for complex iterative workflows.
Container-Specific Engines
Isolate compute environments per container for greater security, compatibility, and governance. This means that instead of all engines being shared across containers by default, users or admins can designate engines to be accessible only within certain containers. This enhancement provides greater privacy and control by enabling teams to isolate their engines for security or operational needs, resulting in improved engine management. This is beneficial for scenarios that require isolated environments.
4. Intuitive Reporting & Monitoring
Curated Workflow Reports
Curated workflow reports now deliver automatically generated insights into recent pipeline and dataflow activities, which is ideal for business users and operations leads who need fast, actionable visibility.
Enhanced Monitoring Across Containers
A centralized monitor now supports dataflows and database workflows, allowing teams to instantly identify issues, optimize performance, and stay in control across environments.
Coming Soon: AI-First Platform Features
Agentic AI Support at Platform level
From test case generation to rule creation, embedded LLMs (including OpenAI and Azure OpenAI support) are now powering key components. What we aim is to slash manual effort while ensuring accuracy.
Logging & Monitoring
New logging capabilities deliver real-time visibility alongside historical insights, providing comprehensive monitoring of platform operations. This enables teams to proactively detect, investigate, and resolve issues faster by tracking application health at both micro and macro levels.
Version Control for Data Sources
Track, roll back, and govern changes to your data source configurations, ensuring improved governance in collaborative environments.
What this means for you?
This update marks a significant shift in how enterprise data teams approach quality, scalability, and automation. Whether you’re modernizing your analytics with Microsoft Fabric or managing hundreds of pipelines across Kubernetes, these new features empower you to do more in a way that is faster and smarter.
Experience these capabilities firsthand by trying out our product.
Contact us today or explore the full release notes to see how Datagaps DataOps Suite can transform your data operations.
Frequently Asked Questions: Spring 2025 DataOps Suite Platform Release
1) What does the Spring 2025 DataOps Suite platform release include?
It adds native Microsoft Fabric support alongside existing SQL Server and Synapse integrations, and brings Oracle Analytics and Publisher support — previously exclusive to the thick client — fully into the DataOps Suite.
2) Does DataOps Suite support Microsoft Fabric?
Yes, as of the Spring 2025 release, Microsoft Fabric is natively supported alongside SQL Server and Synapse, enabling end-to-end testing across the Microsoft data stack.
3) Is Oracle Analytics and Publisher available in the DataOps Suite web platform?
Yes — this release moved Oracle Analytics and Publisher support, which was previously only available in the thick client version, fully into the DataOps Suite.
4) How does this release differ from the Spring 2025 “new features” update?
This post covers platform-level infrastructure advancements (data source connectivity), while a companion post covers individual product feature updates across ETL Validator, BI Validator, and Data Quality Monitoring.
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.
