Systems | Development | Analytics | API | Testing

The explosion of AI-native applications is upon us. With each new week, massive innovations are being made in how AI-centric applications are being built. There are a variety of tools developers need to consider, be it supplying live contextual data via the Model Context Protocol (MCP) or leveraging the new Agent2Agent Protocol (A2A) to standardize how their agentic applications will communicate. The modern AI application can include communication between many different entities, including.

AI builders deploying models into production focus on ensuring well-performing models are available for users. Once the model is live, the focus shifts to optimizing GPU usage for efficient deployment. While GPU machines offer the best performance, they are costly to run and frequently remain underutilized.

Test parameterization allows testers to run the same test case with multiple sets of input data, eliminating the need for duplicate test cases. Instead of hardcoding values, testers define variables that can be dynamically replaced during execution. This approach is essential for testing different scenarios efficiently, such as validating multiple user credentials or input combinations without creating separate test cases for each variation.

Adopting a data lake in combination with the Fivetran Managed Data Lake Service reduces data ingest compute costs by 77% to 95% over traditional data warehouses.

I’ve been in the automation testing game long enough to watch trends come and go. The biggest lesson I gained after all those year is how going back to the fundamentals is usually the "best" best practice. Here’s the thing — buzzwords mean nothing if you can’t trust your test suite. At the end of the day, it’s not about chasing hype. It’s about doing the basics really, really well. So here are 20 automation testing best practices for 2025.

Test automation is about speed, scale, and precision. As software teams ship faster, the pressure on QA increases. Manual testing can’t keep up. You need a way to validate key workflows without slowing things down — that’s where automation test cases come in.

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The rise of natural language query (NLQ) technology in modern business intelligence (BI) and analytics platforms is empowering many companies to streamline data exploration and analysis, and democratize access to insights for more people - not just data experts. But like any technology, the ongoing challenge is to help stakeholders and customers to see the value in using it.

Yellowfin 9.15 is a significant version release that introduces a major update to our Yellowfin Guided NLQ feature in the form of AI-enabled Natural Language Query (AI NLQ), as well as a host of general product enhancements, fixes and security updates. In this blog, we will cover what AI NLQ brings to your embedded analytics deployment, as well some of the other highlights arriving in the Yellowfin 9.15 version release. For the full technical list of updates, please visit our release notes page.