Build a BigQuery AI agent with ADK & Cloud Run
In this video, Mazlum Tosun walks Martin Omander through building and deploying an AI data agent. Watch along as the team takes a BigQuery database (the company's data goldmine) and open it for plain English questions, using Agent Development Kit (ADK) and Model Context Protocol (MCP). Resource links: Speakers: Martin Omander, Mazlum Tosun Products Mentioned: BigQuery, Agent Development Kit, Cloud Run.
The Analytical Work Business Users Won't Hand Over to AI
The AI-analytics category has one operating assumption: AI’s job is to do more of what humans currently do. Adoption is measured by delegation surface. Success is when AI takes the whole task. Ask the executives running AI daily what they refuse to delegate, and you get a different story. Actually, you get the same story, from operators who do not know each other, running different companies, across different functions.
Is your data stack ready for AI?
Most data stacks weren’t built for AI agents. Take the ODI Assessment to find out if yours is ready.
Beyond Migration: Elevating the SI Role to Strategic AI Architect
For years, the mandate for System Integrators (SIs) was clear: lead the "cloud-first" migration. The promise was lower costs, greater agility, and seamless innovation. But for many enterprise customers, that promise remains unfulfilled. Instead of agility, organizations have inherited a complex, fragmented data estate. Data is siloed across on-premises legacy systems and multiple public clouds, creating governance headaches and inflating infrastructure costs.
How to Ingest and Reconstruct Multiple Unrelated CSV Exports or a PostgreSQL Dump from an Acquired Legacy System
You ingest and reconstruct multiple unrelated CSV exports or a PostgreSQL dump from an acquired legacy system by first mapping the dump's underlying schema and relationships, then building a staged pipeline that loads raw files or tables as-is, reconstructs relationships through keys, and only then applies business logic to produce clean, usable records.
Natural Language to Data Pipeline: How to Build Migrations Without Writing Code
You build a data pipeline from natural language by describing the source, destination, and required transformations in plain English to a platform with a prompt-to-pipeline feature, which then generates a draft pipeline with inferred field mappings, transformations, and a schedule for you to review and adjust. This guide is for operations teams, data analysts, and junior team members who understand the desired outcome of a migration but don't write SQL or Python.
Ep 82 | Behavior Change: How Startups Are Making AI Stick
Behavior change is the biggest hurdle in AI adoption. AI only creates value when people make it part of their everyday work. In this episode of The AI Forecast, Paul Muller sits down with Varun Puri, CEO and co-founder of Yoodli, to discuss why successful AI adoption starts with changing how people work. Drawing on his experience at Google, Google X, and as the founder of an AI startup, Varun shares practical lessons on embedding AI into everyday workflows and building habits that stick.
A Google Data Cloud Leader's Formula for Token-Efficient AI
For most enterprises, autonomous AI agents still feel like a risk waiting to happen. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson to explain what it takes to build the data foundation that trustworthy agentic AI depends on. He breaks down how organizations can finally activate the 90 percent of enterprise data that's unstructured, why tokenmaxxing is the wrong way to measure AI value, and how open standards like Apache Iceberg are helping leaders tear down fragmented, multi-cloud data silos and unify data across clouds.
5 ways Fivetran accelerates mergers and acquisitions
From identifying targets with proprietary market data to closing on time and separating cleanly in a divestiture — having all of your data in one place can make or break your M&A.