Systems | Development | Analytics | API | Testing

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.

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.

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.

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.

Preventing Kafka war rooms: with self-service troubleshooting

Most Kafka war rooms don't start at 2am in production. They start weeks earlier, when an engineer couldn’t explore a topic, see partition distribution, a quota or a config and shipped hoping for the best. That's the Platform Engineering waiting room: developers building apps waiting in a queue of tickets, with no self-service visibility into Kafka. That release triggers a P1 later.

How To Unlock AI Data Anywhere (Even On-Prem) for Regulated Industries

Most AI content assumes your data is in the cloud. But for a meaningful segment of enterprises, cloud-only AI tools block them at the pass. For regulated industries like manufacturing and healthcare, data residency requirements, compliance mandates, security policies, and simple operational reality mean sensitive data must remain on-premises.

The AI Opportunity Gap Is Real. It's Growing. And It Is Not About Access to Tools.

In the first part of this series, I argued that discernment, the ability to recognise when an AI-generated answer is wrong, is becoming one of the most valuable capabilities inside an organisation. The question this piece addresses is simpler and harder: who is actually being given the opportunity to develop it? The AI opportunity gap is real. It is not primarily a gap in access to tools. It is a gap in permission. And I believe that gap starts earlier than most leaders realise, often in school.

Rethinking AI Governance with Lisa Pent #Cloudera #Shorts #AIGovernance #TechRisk

New AI tools are coming out faster than ever, and your employees are working hard to stay at the cutting edge of new technology. Where does governance come into play? Explore how to govern AI without slowing down innovation in the latest episode of The AI Forecast, sponsored by Cloudera.

How Endpoint Clinical Closed the Embedded Analytics Revenue Gap

I'll be honest: one number from the latest embedded analytics research stopped the entire planning conversation for this webinar. 57% of teams with embedded analytics report no measurable business impact, and that means not low impact or underwhelming impact, but no measurable impact at all.