ChatGPT Made AI a Tool for Everyone - Now Data Infrastructure Needs to Catch Up

When ChatGPT entered the mainstream, it didn’t just change how people use artificial intelligence — it changed who gets to use it. By abstracting away the complexity and making the interface simple and intuitive, OpenAI opened the floodgates. Now, instead of AI being the exclusive domain of engineers and data scientists, it’s being actively explored by product managers, marketers, revenue operations leaders, and customer experience teams.

ETL Testing Tools for Modern Data Quality Assurance

In a modern data stack, reliability isn't optional, it's a requirement. Data teams are tasked with building pipelines that extract from dozens (sometimes hundreds) of disparate sources, transform data under strict business logic, and load it into analytics-ready destinations. But even the most well-architected ETL workflows can fail silently without rigorous testing.

ETL for LLMs to Build Context-Rich Pipelines for Generative AI

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.

Demo: Real-time mortgage underwriting AI agents with Confluent, Databricks, and AWS

This demo showcases a use case for a mortgage provider that leverages Confluent Cloud, Databricks, and AWS to fully automate mortgage applications—from initial submission to final decision and offer. New to Confluent? Experience unified Apache Kafka and Apache Flink with a free trial.

Event-Driven AI Agents: Why Flink Agents Are the Future of Enterprise AI

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.

Presenting Astera AI: The Agentic Data Stack For Your Enterprise Data Management

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?

Top 10 Reasons to Move to Qlik Cloud

If you're still using Qlik Sense on Windows, it's time to consider a smarter, more powerful alternative. Qlik Cloud Analytics is purpose-built for modern data needs, offering a next-generation platform that combines AI-driven insights, automation, and enterprise-grade security—all without the overhead of traditional infrastructure. In this video, we’ll explore the top 10 reasons why migrating to Qlik Cloud is not just an upgrade, but a strategic move to accelerate innovation, enhance collaboration, and reduce costs across your organization.

From Pawns to Pipelines: Stream Processing Fundamentals Through Chess

We understand new concepts by linking them to familiar ones. These analogies aren’t just helpful; they’re how we think. For me, that something familiar is chess, and I’ll use it to explain some of the core ideas behind stream processing—a concept that requires a shift from seeing tables as static snapshots to treating tables as materialized projections of a continuous stream of changes.