Systems | Development | Analytics | API | Testing

Large Language Models (LLMs) like GPT-4, Claude, and LLaMA have reshaped the way businesses think about intelligence, automation, and human-computer interaction. But the performance of an LLM hinges entirely on what powers it: data. And that data must be systematically collected, cleaned, enriched, and delivered—a task owned by the ETL (Extract, Transform, Load) pipeline.

The evolution of artificial intelligence (AI) in the enterprise has reached an inflection point. While the early days of generative AI focused on chatbots responding to human prompts, today's enterprise AI agents are fundamentally different—they're event-driven, autonomous systems that continuously process streams of business data, make real-time decisions, and take actions at scale.

APIs are the digital lifelines powering modern applications, microservices, IoT devices, and everything in between. They act as the universal translators of data, ferrying information between diverse software platforms. API security encompasses the technologies, practices, and protocols dedicated to protecting these invisible workhorses from unauthorized access, data breaches, and malicious misuse.

Are you aware of the fact that India’s overall fintech market opportunity is estimated to be $1.3 Tn by 2025, growing at a CAGR of 31% during 2021-2025? This one statement paved the way for the FinTech revolution in India and is still growing… In just a few years, the emergence of fintech-powered technologies has revolutionized the financial sector, affecting how both large and small firms deal with their finances and transactions.

Different browsers can display the same website in completely different ways. What seems great in Chrome can be broken in Safari, and what works in Firefox just might fail in Edge. Cross browser testing ensures that your website works consistently across all the browsers before your potential users see problems. That way, the team can catch browser-specific issues ahead of time, preventing them from affecting the user experience or tarnishing the outlook of your brand.

As enterprise data increases in volume, variety, and velocity, the need for a new data architecture is becoming clearer. As AI moves from generative to agentic, can enterprises also envision and adopt an agentic data architecture? It’s true that we’re already seeing AI agents implemented in functions such as customer support and marketing. But what if we could do the same for data management?

This article originally appeared on DevPro Journal. We’re sharing it here for our audience who may have missed it. For years, APM tools were everyone’s go-to solution for understanding how software behaved in production. And for a time, they worked, because architecture was simpler. Developers owned the backend, the frontend, and the data layer. Everything lived inside a monolith. If something went wrong, they could trace it through their codebase and fix it.

Testing has evolved far beyond scattered spreadsheets and disconnected tools. Yet even with modern platforms in place, teams still run into bottlenecks, especially when fundamental tasks like test case creation are handled manually. It involves combing through acceptance criteria, writing out each step, and reviewing everything for gaps. Repeating that across multiple user stories quickly drains time and slows progress – it’s repetitive, time-intensive, and prone to inconsistency.

As there are many questions to answer, we have split this blog in two parts. In part 1 of this blog, we explored how Qlik is using data and insight to drive decisions and actions. In this second part, we will focus on how to overcome the challenges along the way and set up for success for the future.

When developing software applications, data storage is a key concern. The reality is that your first concern should be the data model you choose, which in turn affects how you store data. Generally speaking, this means deciding between SQL and NoSQL databases. In this article, you will learn how to use MongoDB, a popular NoSQL database, in a Flask application. First, you will learn why MongoDB is a good choice, and then we will implement a practical hands-on project using MongoDB in Flask.