How to assess your organization’s AI readiness
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A Tale of Two Houses: A Comparison Between Streamhouse and Lakehouse
Compare streamhouse and lakehouse data architectures, learn which workloads each one fits and how they complement each other.
What’s New in Q4’26 Confluent Data Streaming Platform: The Most Complete, Performant, and Open Foundation for Streamhouse
Learn about Lightning Tables, Managed Model Inference, multi-tenancy for CPC, Tableflow for Google Cloud, and other Confluent Cloud, Confluent Platform, and WarpStream updates.
Your Business, Always Current. Streamhouse - The Data Architecture for the Age of AI.
A Streamhouse is a data architecture that continuously captures, processes, governs and serves current business state and context across distributed systems, making them available to applications, AI agents, and analytics.
How to put generative AI directly into a dbt pipeline with plain SQL
Google Cloud on wiring BigQuery's AI SQL functions into dbt pipelines, from sentiment scoring to LLM-generated product briefings.
How KOHO built a Claude-powered self-serve analytics tool on years of dbt governance
KOHO explains why AI self-serve analytics needs a strong data culture and a governed dbt foundation first.
Why an AI agent's revenue query was $3.5 million wrong, and how AWS fixed it with dbt Semantic Layer
A plausible AI revenue query was $3.5 million wrong. AWS explains how business context helps prevent those errors.
Why most of your dbt models aren't in a hurry, and what that's worth in Snowflake credits
Most models in a dbt DAG aren't on the critical path. Espresso AI and Authentic Insurance on turning that slack into real savings.
How Ecolab used an AI migration agent to shrink 84,000 lines of legacy SQL by 6x
Ecolab and Datafold used an AI migration agent to rebuild a decade of tangled Snowflake logic as clean, documented dbt models.
How Checkr cut an hours-long metrics investigation to minutes by engineering AI context on dbt and Omni
Checkr gave its AI agent a structured dbt context layer, cutting a support-metric investigation from hours to minutes.
Ship AI agents like tables: dbt materializations for Snowflake CoWork
Under Armour turned Snowflake AI agents into dbt models, giving every agent the same review and CI process as every table.
Why a semantic layer is the difference between a 96% and an 80% accurate AI analyst
Why conversational analytics stalls and the data fundamentals that make it reliable.
With great context comes great autonomy
AI agents need governed context, not just more data, to be trusted.
Why dbt Labs thinks AI agents will finally make the semantic layer stick
Why AI agents can make semantic layers stick, if teams can solve the trust problem.
How KOHO built a Claude-powered self-serve analytics tool on years of dbt governance
KOHO explains why AI self-serve analytics needs a strong data culture and a governed dbt foundation first.
What Okta learned governing context for AI agents at scale
Okta rebuilt how it governs context for AI agents, using dbt as the single source of truth.
Inside dbt Wizard, the AI agent built to show its work
dbt Wizard's designer and engineering lead show how the agent validates its own work before code ever reaches production.
dbt v2 lands: faster parsing, one shared adapter layer, and a docs experience that skips the giant manifest.json
dbt v2 bundles adapters, speeds up parsing, and replaces manifest.json with smaller, queryable files for people and AI.
Showing our work: a controlled experiment in context engineering
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.
How Ramp's internal AI agents crossed a million sessions
Ramp shares the agents, sandboxes, and dashboards it built by treating internal AI as analytics engineering.