Systems | Development | Analytics | API | Testing

After two weeks of immersion at the Databricks Data + AI Summit and Snowflake Summit, it’s clear that the pace of innovation is accelerating faster than ever. Both events showcased groundbreaking advancements in AI, data orchestration, and table formats, with Iceberg, catalogs, and hybrid customer personas emerging as the key themes. Below are some analysis on the trends shaping the modern data ecosystem.

We’re at it again, bringing more incremental improvements to Kong Mesh!

Devoxx Poland 2025 felt different. Not because of revolutionary new frameworks or another "this changes everything" moment, but because of what didn't happen. The conference had an unusual dose of pragmatism, skepticism, and – dare we say it – common sense. Maybe it's because developers are asking the right questions: "Does this solve a problem?" and "What happens when this inevitably breaks?" Here's what emerged from the sessions we watched, and the people we spoke to.

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.