Systems | Development | Analytics | API | Testing

The era of generative AI is upon us, and large language models (LLMs) like Claude are demonstrating incredible potential to revolutionize how businesses operate and interact with customers. However, to truly unlock this potential, AI needs secure and standardized access to the wealth of information and services locked within enterprise systems. This is where standards such as Model Context Protocol (MCP) come in, offering a powerful way to make enterprise resources AI-consumable.

As we explored in our previous post, SAP’s ecosystem makes it costly and complex to centralize data, especially when organizations need to blend SAP and non-SAP sources for unified analytics. Between restrictive licensing, limited compatibility, and constrained export options, SAP’s architecture often creates more roadblocks than results.

Qlik has just been recognized - for the 15th year - as a Leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms, and I am delighted to confirm Qlik is moving further up and to the right. The message is clear: we’re executing on the right vision, and the market is taking notice. It’s also driven by a commitment to innovation, trust, and above all, our customers.

When people search for "best analytics tools," they usually find lists that include the same names: Depending on the month, some new or unexpected names may also appear. Regardless, they typically lead to a quick summary, showing off their respective pros and cons for each, and a link to their pricing pages. Useful basic information, sure, but what they do not do is show what matters most: Showing what your team needs to own, understand, and protect.

APIs need strong security. They often handle sensitive data like financial records or health information. To protect this data while meeting privacy rules (like GDPR and HIPAA), fine-grained access control (FGAC) is key. FGAC uses OAuth scopes and claims to manage API access.

BigQuery applies enhanced vectorization to aspects of query processing such as filter evaluation and data encodings and optimization techniques.

When you use loops in Python, there are a lot of times when you don’t just want the item from a list, you also want to know where that item is in the list. For example, going through a list of names and needing to show their position, like “1. Alice”, “2. Bob”, and so on, or you could be building a menu where each option needs a number next to it. In these situations, Python’s enumerate() function is very helpful.

Katalon officially signed a Memorandum of Understanding (MOU) with RMIT University Vietnam. This collaboration aims to bridge academic learning with real-world software development and software testing expertise, equipping students with an adaptive mindset, future-ready skills and AI tools. Leaders and representatives from RMIT Vietnam and Katalon at the MoU signing ceremony.

NGINX One provides a modern solution for enterprises to manage infrastructure at scale across globally distributed systems. The platform has built-in tools for essential performance and uptime metrics, giving DevOps teams visibility into the health of their NGINX instances. But for effective API observability and analytics, you have to go beyond infrastructure metrics.

In this episode, Emmanuel Paraskakis, CEO Level 250 breaks down the core responsibilities of an API product manager. Speaking from his experience in product management for over fifteen years, Emmanuel distinguishes an API product manager’s focus from conventional product roles, underscoring their critical importance in building scalable digital platforms.