Systems | Development | Analytics | API | Testing

Analyzing logs in data pipelines is essential for maintaining system performance, troubleshooting errors, and ensuring compliance. Here's what you need to know: Why It Matters: Logs help identify bottlenecks, resolve errors, and optimize performance. They are also critical for audits and compliance. Challenges: High log volume, varying formats, and security risks make analysis complex. Solutions: Standardize log formats with timestamps, log levels, and metadata.

Innovation isn't a buzzword at ThoughtSpot; it's the lifeblood of our business. It’s woven into our culture, driving us to constantly push boundaries and deliver exceptional value to our customers, our partners, and our team. Recently, I had the privilege of sitting down with a powerhouse panel of senior functional leaders for an “Innovation Spotlight” session. Our goal? To share how bold thinking translates into real-world impact across the many different facets of our organization.

Testing consumes 20-30% of your development budget, yet some QA platforms still operate like it's 2015. While developers embrace AI to ship features in hours instead of weeks, QA teams struggle with manual testers, brittle scripts, and costs that spiral out of control. The numbers tell the story: Companies using AI-driven QA achieve 85% better coverage and 30% cost reduction. Test creation is now 80% faster.

The automation testing market has exploded to $33.13 billion in 2024 and is projected to reach $211.16 billion by 2037, driven by the relentless demand for faster software delivery and enhanced quality assurance. At the heart of this transformation lies automated test case management—a game-changing approach that's revolutionizing how development teams create, execute, and maintain their testing strategies.

As I recently transitioned to Qlik's Professional Services team to lead Advisory Services, I embraced a clear mission: to engage in high-level discussions with business stakeholders that drive faster value and enhance your return on investment (ROI). By employing a structured 10-step approach grounded in a repeatable agile methodology, we are actively enabling out customers to overcome challenges and achieve their strategic objectives.

Securing APIs requires managing who can access resources and under what conditions. Two primary models stand out: Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC). Here's the key takeaway: RBAC assigns permissions based on predefined roles, making it simple to manage in structured environments. ABAC evaluates multiple real-time attributes for dynamic, granular control, ideal for complex or evolving scenarios.

In the constantly changing landscape of software development, shipping features fast and consistently is a must. Automated testing is one of the central pillars that enable this agility. And for writing solid, scalable, and comprehensible automated tests, Python is a go-to for both developers and QA engineers alike.

Regression testing tools for web applications help you ensure that updates to your code do not break existing features. They automate repetitive test cases so your team can release faster and with more confidence. Choosing the right tool matters. There are many automated regression testing tools and web app regression testing software available. Some focus on speed, some on cross-browser testing, and others on AI-powered features that reduce maintenance work.

Automation testing for websites is one of the fastest ways to improve software quality while speeding up delivery. It removes repetitive manual work and ensures that every release is tested thoroughly before going live. Effective automation testing becomes even more powerful when paired with a solid test management strategy. The benefits of test management in testing process include better organization, clearer visibility, and improved collaboration across teams.

In the last two parts of this series, we discussed How to Strengthen a ReAct AI Agent with Kong AI Gateway and How to Build a Single-LLM AI Agent with Kong AI Gateway and LangGraph. In this third and final part, we're going to evolve the AI Agent with multiple LLMs and Semantic Routing policies across them. In this blog post, we'll also explore new capabilities introduced in Kong AI Gateway 3.11 that support other GenAI infrastructures.