A recent study by PwC suggests that AI could contribute up to $15.7 trillion to the global economy by 2030, with automation playing a key role in boosting efficiency and innovation. AI agents are central to this transformation, streamlining workflows, handling repetitive tasks, and enabling data-driven decision-making. From virtual assistants in customer service to intelligent fraud detection in finance, these agents are reshaping industries and driving business growth.

Not long ago, I wrote about a growing problem in enterprise AI: agents that don’t talk to each other. You’ve got a customer relationship management (CRM) agent doing its thing, a data warehouse agent crunching numbers, a knowledge bot quietly surfacing documents—but none of them are sharing what they know. Instead of a smart, connected ecosystem, we’re stuck with isolated pockets of intelligence: an island of agents.

Choosing the right storage format is crucial for optimizing performance, cost, and flexibility when working with cloud data. While file formats like Apache Parquet and Avro have been popular choices for storing data in data lakes, in recent years a new category called table formats has emerged to provide more management capabilities on top of these files. Among these, Apache Iceberg has been gaining significant adoption and momentum. So what exactly is Iceberg and why does it matter? Let’s dive in.

Two years after Seagate first shared their AI and MLOps success story, the data storage leader is now revealing how far they've come since then. In this blog post, you’ll see how the team manages thousands of AI models in production with only a few team members. This is thanks to their AI factory, whichdoes the heavy lifting of automated processes like monitoring, testing, mocking and more.

A new AI-assistive chat experience powered by Gemini in BigQuery data canvas can help with everything from data exploration to visualization.

You stare at last quarter’s sales data in your spreadsheets, but when your CEO asks “What should we do differently next month?” you freeze. The numbers are there, but they don’t tell a story. They don’t point to clear actions. Despite all the data you collect, you still make the most important decisions based on gut feeling. Sounds familiar? You’re not alone.

Let’s be real—clients don’t care about how much effort you put in. They only care about results. If you’re not delivering clear, real-time performance insights with zero fluff, you risk losing their trust—and their business. That’s why a good client dashboard software is a necessity. It can take the guesswork out of reporting, and give your clients a crystal-clear view of their campaigns without endless emails or confusing spreadsheets.

Before we delve into agentic RAG and AI agents, let’s take a moment to acknowledge that the world of artificial intelligence is evolving at a tremendous pace. From the initial excitement surrounding large language models (LLMs) to the practical application of generative AI (Gen AI), businesses are constantly finding new ways to automate tasks and innovate faster.

Whether we like it or not, when it comes to building data pipelines, the ETL (or ELT; choose your poison) process is never as simple as we hoped. Unlike the beautifully simple worlds of AdventureWorks, Pagila, Sakila, and others, real-world data is never quite what it claims to be. In the best-case scenario, we end up with the odd NULL where it shouldn’t be or a dodgy reading from a sensor that screws up the axes on a chart.