Systems | Development | Analytics | API | Testing

The enterprise AI landscape is transforming rapidly. On November 4th, Snowflake Intelligence—a breakthrough agentic AI platform built on the Cortex AI suite—ushers in a new era of business insights. With support for natural language querying (NLQ) and generation (NLG), organizations can interact with their data conversationally, unearthing real-time intelligence directly from complex, multimodal sources.

1. Organizations have moved beyond pilots and are embedding LLMs into production workflows across customer support, finance, security, and software delivery. 2. LLM observability mitigates risks like hallucinations, bias, compliance breaches, and runaway costs. 3. LLM observability requires prompt/response tracking, hallucination detection, drift monitoring, RAG pipeline visibility, and long-term context tracing. 4.

At this year’s Big Data London, Keboola CEO Pavel Doležal sat down with data expert Christina Stathopoulos on EM360Tech’s Don't Panic, It’s Just Data podcast to explore what’s next in enterprise data automation. The conversation centered on a quiet but powerful shift in how AI, particularly large language models (LLMs), is changing the way organizations interact with their data platforms - not just by querying them, but by acting on them.

Across industries, AI systems are being scrutinized under new laws that demand proof of fairness, transparency, and human oversight. Hemraj Bedassee , Delivery Excellence Practitioner,

Traditional API Gateways: Excellent for routing, auth, and microservice traffic; poor at AI workloads. Limitations: Can't track tokens, manage streaming responses, enforce content-level security, or use semantic caching. AI Gateways Purpose-built for LLMs with: Architecture Recommendation: Layered approach: Benefits: Lower costs (20--40%), better performance, centralized governance, future-proof AI infrastructure. Market Context.

If you recall part one of this blog post, we were going to use ChatGPT in parallel with how we would work to cover these aspects of performance testing. We left the first part of this blog post at the point at which we had compared Requirements Gathering and Risk Assessment, we will pick this post up by looking at Script Creation before concluding with Results Analysis. Our performance testing tool of choice will be JMeter.

If you believe many articles online you would believe that automation in testing will soon be defined, managed and executed by Artificial Intelligence (AI). AI is embedded in many organisations technology landscape and to think that this model will change is shortsighted. AI is here to stay undoubtedly in one form or another, but should it be responsible for the automated testing of your applications under test?

Census makes it easy to turn warehouse data into AI-ready vectors — without building a single pipeline.

In today’s fast-moving vibe coding world, speed often wins. Developers spin up APIs, microservices, and pipelines in minutes. But speed comes with a cost: consistency, compliance, and long-term maintainability of APIs often lag behind. If you’ve ever looked back at a project and realized that naming conventions slipped, security best practices were inconsistently applied, or APIs drifted away from your organizational guidelines, you’re not alone.

Artificial intelligence is no longer a futuristic concept, it’s a business necessity. The rise of generative AI (Gen AI) has transformed it from a buzzword into a defining force reshaping industries at every level. What was once viewed as an experimental technology is now a key driver of competitiveness, innovation, and efficiency across the global economy.