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DataOps Suite Update Spring 2025: Platform Advancements

DataOps Suite Platform Advancements Update

In the 2025 Spring release of the Datagaps DataOps Suite, we’ve introduced several platform enhancements focused on delivering speed, visibility, and control to enterprise data teams. From centralized engine management to expanded data source support, this release equips your data operations with the flexibility and automation needed to scale confidently. 

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 Import of Dataflows
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.

Container-Mapping

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.

Monitoring Across Containers

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. 

Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  Datagaps 
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