AI-first Data Patterns
Darshil Parmar mapped a 12-pattern playbook for enterprise data (ETL/ELT, CDC, Lakehouse and more) and argues Lakehouse is the industry direction — framing data infra as the backbone for AI/LLM systems outlined. That lines up with architecture analysis showing AI must be woven into the stack (real-time flows, semantic search, RAG) rather than bolted on — meaning feature stores, catalogs and validation become table stakes argued.
Darshil Parmar runs a YouTube channel with ~201K subscribersyoutube.com. His Substack lists 4.1K+ subscribers for data-engineering posts and tutorialssubstack.com. Databricks published a foundational "What is a data lakehouse?" explainer in January 2020 to define the architecture approach vendors build ondatabricks.com. Snowflake positions "Lakehouse Analytics" as a core use case for governed AI workloadssnowflake.com and Microsoft documents lakehouse patterns for Azure Databricks in its product guidancelearn.microsoft.com. Architecture & Governance’s EA primer argues enterprise architecture must shift toward "architecting enterprise intelligence" to operationalize AI across systemsarchitectureandgovernance.com. A technical review of the RAG stack on arXiv by Dean Wampler et al. models Retrieval‑Augmented Generation as an integrated stack component that encodes provenance and trust requirements for LLM systemsarxiv.org. Databricks announced major Unity Catalog enhancements at Data + AI Summit 2025 for centralized governance and discoverydatabricks.com, and Databricks documents a Feature Store for registering feature tables and serving low‑latency features across workspaceslearn.microsoft.com. Great Expectations is promoted by its maintainers as an open‑source framework for automated data validation, profiling, and auditable pipeline checksgreatexpectations.io. The Model Context Protocol (MCP) has public specification and setup docs from the MCP projectmodelcontextprotocol.io and documented enterprise pilots—such as Block’s engineering write‑up—show MCP being trialed to expose controlled data and tool endpoints to agentsblock.github.io.
