Lakehouse Architecture
An architecture that combines data-lake economics (cheap object storage, open file formats) with warehouse guarantees (ACID transactions, schema evolution, time travel) so analytics, AI retrieval, and machine learning all read from the same trusted tables.
Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.
What it is
The term, popularized by Databricks, describes a stack where Parquet files in object storage (S3, GCS, Azure Blob) are managed by an open table format — Apache Iceberg, Delta Lake, or Apache Hudi — that adds transactions, schema evolution, partition evolution, time travel, and concurrent writes. The result has the cost and openness of a data lake with the correctness guarantees of a warehouse.
- Object storage + Parquet for cost and openness
- Iceberg, Delta, or Hudi for ACID and schema evolution
- Compute pluggable: Spark, Trino, DuckDB, Snowflake, BigQuery
- Time travel and partition pruning for query performance
Why it matters for AI
Retrieval pipelines, analytics dashboards, and ML training read from the same tables. There is no longer a 'warehouse copy' and 'AI copy' that drift apart. When a number on the executive dashboard differs from what the agent cited, the disagreement is a real bug, not a sync delay.
What we evaluate
Table format choice depends on your existing stack and write patterns: Iceberg has the broadest engine support and the cleanest spec; Delta is the obvious choice on Databricks; Hudi shines on streaming upserts with frequent CDC. Engagements often start with a small migration pilot to surface the actual operational differences before committing.
Related resources
Explicit agreements between a data source and the systems that depend on it — what shape, how fresh, who owns it, what counts as broken — so pipeline failures become attributable instead of mysterious.
A navigable map of every system your data lives in — schemas, documents, code, tickets, events, owners, and permissions — so an AI agent can find the right source and respect the right access boundary.
Contracts, validation, lineage, freshness, and ownership for the data your AI reads from — not a one-time cleanup project, an ongoing operating discipline.