2 posts tagged with "data-warehouse"

Your data lake and warehouse already hold the answers your business needs. The missing layer isn't more data — it's an intelligent orchestration layer that lets AI agents query, reason, and act on that data reliably.

This post walks through a production-ready architecture that uses dbt as a semantic manifest, Model Context Protocol (MCP) servers as the access layer, and multiple specialized agents to turn your existing Snowflake, Redshift, or BigQuery investment into an active, AI-driven intelligence system.

Every modern data strategy starts with the same question: where does the data live, and in what form? The answer determines everything downstream — what analytics are possible, how fast queries run, what AI workloads you can support, and how much the infrastructure costs to operate.

The three dominant paradigms — data lake, data warehouse, and data lakehouse — are often presented as competing alternatives. In practice, most mature data platforms use all three in combination. Understanding what each is optimized for helps you decide which layer owns which data at each stage of its lifecycle.