Snow Report: What's Happening At Snowflake in June
Summit 2026 delivered major announcements across AI agents, real-time analytics, and enterprise data.
Meeting Data (and Analytics) Engineers Where They Are: Introducing the dbt Adapter for Confluent Cloud
dbt is the most commonly used tool by data engineers to define SQL transformations (as models), write tests, generate documentation, and deploy through CI/CD and now it’s available with Confluent Cloud too! The magic of dbt is that it brings the engineering rigor to modern data work and data engineering, regardless of the underlying compute source - Snowflake, BigQuery, Databricks, Redshift or Confluent. You can find out more about the launch in our Q2 Confluent Cloud Launch post and the keynote.
How Structured Content Improves Financial Product Communication
Financial product communication has to be clear, accurate, and easy to understand. Customers often compare banking products, insurance options, investment services, loans, credit cards, payment solutions, and savings accounts before making a decision. Each product may include detailed information about fees, eligibility, benefits, terms, risks, application steps, and support options. When this information is presented in a confusing or inconsistent way, customers may struggle to understand what a product offers and whether it is right for their needs.
AI ambitions at risk: 72% of IT leaders say poor infrastructure is stalling AI growth
Confluent's 2026 Data Streaming Report finds the biggest barrier to AI growth isn't investment, but the infrastructure needed to support it.
Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail
AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments. Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.
Reporting Intelligence UI Flyover
See how Reporting Intelligence from insightsoftware transforms the way finance teams build, manage, and distribute ERP-connected reports, without IT involvement, data engineering, or infrastructure overhead. This walkthrough covers the full product experience, from self-serve report building in Excel to cloud-based scheduling and distribution, embedded Lineos AI assistants, and the autonomous Intelligence Layer that monitors your KPIs and surfaces insights around the clock.
How to build your first AI agent
A step-by-step guide to laying the groundwork, choosing the right use case, and getting a practical start with AI.
The internal war over who owns AI.
There is a massive boardroom fight happening right now over who gets to control AI. Should it be IT? A centralized lab? The executives? Boris Rabkin from Ligentia drops a truth bomb: AI belongs wherever value is actually created. If your AI strategy is locked inside an isolated corporate lab instead of in the hands of your product, engineering, and customer teams, it’s going to fail. Full episode out now!
Stop Rebuilding Data Models From Scratch: Meet SpotterModel
Your data engineering team shouldn't be the bottleneck between a business question and a governed answer. SpotterModel turns a natural language prompt into a deployable data model. This release does the heavy lifting on complex calculations, and lets you roll back to any previous model state, anytime, so a bad change never costs you hours of rebuilding. It maps your relationships, dimensions, and measures instantly, and you stay in control of table selection and the build process the whole way.
Build vs Buy Streaming for Real-Time RAG: 2026 Guide
Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.