What happens to data work when AI changes everything? The hosts of The View on Data podcast share what's shifting and what isn't.

Learn how to connect dbt, when to migrate, and what the tradeoffs are for your data team.

Building the future of analytics engineering, together.

Explore key data transformation types for ML, including cleaning, scaling, feature engineering, and validation.

Explore common data pipeline architecture patterns—from ETL and ELT to batch, streaming, and semantic layers.

Learn how a semantic layer gives AI systems the consistent, governed foundation they need.

Explore ETL vs ELT and how modern transformation tools power scalable data pipelines.

Anders Swanson explains what data teams can realistically expect when attempting to run on top of Iceberg in production.

Metadata management improves discovery, governance, performance, and trust in modern data systems.

ETL consolidates fragmented data, enforces quality, and satisfies compliance requirements modern organizations depend on.

NBIM cut runtimes 30–40% in 3 months with the dbt Fusion engine and State-Aware Orchestration—without heavy optimization.

Improve data quality with testing, metrics, automation, and a scalable governance framework.

ETL, ELT, batch, CDC, reverse ETL—learn the key data movement patterns and when to use each.

Learn how transformation methods improve data quality, consistency, and analysis at scale with dbt.

Proven strategies to improve data quality with testing, governance, and scalable analytics workflows.

Discover how AI accelerates data lineage with automated docs, testing, and scalable governance.

AI is reshaping data pipelines—driving real-time data, automation, and AI-assisted modeling at scale.

Unify metrics across BI and AI with Omni + the dbt Semantic Layer. Define once, query everywhere, eliminate semantic drift.

Zscaler built an AI-powered, multi-agent PR review system that uses dbt’s structured context.

Data ins and outs for 2026: simplify workflows, avoid over-engineering, and rethink how data teams use AI.

Learn how AI accelerates SQL modeling, testing, documentation, and performance optimization in modern data teams.

With intelligent orchestration and optimization, we achieved a 64% reduction in compute costs and simplified our job architecture.

Learn how tools like Fusion’s state-aware orchestration deliver real business value.

Power reliable Cortex and Snowflake Intelligence experiences with dbt and Snowflake Semantic Views.

Not sure if you need a data observability platform? Learn the signs, tradeoffs, and business impact of investing in one.

dbt Core v1.11 is GA with first-class UDFs, clearer project validation, and performance improvements across adapters.

Learn how to provide structured context to AI systems with a dbt-powered MCP server.

Data mesh decentralizes data ownership and reduces bottlenecks with self-service platforms and federated governance.

Analytics engineering extends beyond modeling to collaboration, testing, documentation, and scaling analytics workflows.

Learn how a data control plane centralizes metadata, governance, & collaboration to make distributed data architecture manageable.