Systems | Development | Analytics | API | Testing

In JavaScript, variables are fundamental building blocks. With the introduction of ES6, developers gained more options – let and const – alongside the traditional var. Understanding the distinctions between these declarations is crucial for writing clean, efficient, and bug-free code. Regardless of whether you are new to programming or not, knowing how var, let, and const operate can help you avoid a lot of common mistakes and write better JavaScript.

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.

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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.

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.

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.

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.

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.

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.

Performance testing is essential for ensuring that web applications can handle real-world user loads. A critical part of setting up a reliable test is configuring the ramp-up period correctly in JMeter. Whether you’re a business owner, product owner, developer, or DevOps professional, understanding and configuring this parameter can mean the difference between a test that reflects true performance and one that misses crucial performance problems.