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An AI-ready warehouse gives an assistant a traceable route from source records to a governed analytical answer. Keep raw data for audit and replay, clean and conform it, model business processes as facts with explicit grain and shared dimensions, then expose canonical metrics through a narrow semantic interface with definitions, synonyms, lineage, provenance and permissions. Do not point an LLM at a pile of raw tables and expect it to infer joins or metric logic.
This design makes natural-language analytics safer: the model can identify the right measure, apply the valid time context, respect row-level security and explain where an answer came from. The following architecture and implementation sequence show how to build that path.
What does an AI-ready warehouse look like?
An AI-ready warehouse has four connected layers, each with a distinct responsibility. The durable gold model remains reusable for BI and data science; an AI-facing surface narrows that model into documented, permission-aware objects that an agent can query without guessing.
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The bronze or raw layer stores source records with ingestion timestamps, source identifiers, batch or event metadata and enough information to replay a load. Preserve the original payload when practical. This layer is not an end-user interface, but it lets engineers audit a surprising answer, reprocess a failed transformation and prove which source version produced a value.
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Silver: clean, validate and conform
In silver, cast types, apply business validations, remove duplicates, standardize codes and reconcile identifiers across systems. Retain the history required by the business rather than overwriting every change. A customer status, product category or territory that changes over time needs an explicit rule for whether reports use the value at the event date or the current value.
Gold: publish business-ready structures
Gold contains consumption-ready star schemas, marts, aggregates and other curated objects. Microsoft’s medallion guidance describes this layer as the home for consumption-ready star schemas, data marts and preaggregated tables, with semantic models sitting over curated gold data. Gold should be optimized for stable business questions, not for mirroring application tables.
AI-facing semantic surface: expose only what can be explained
Create a metric view, semantic model or purpose-built view over gold. Give it business-friendly names, explicit measures, documented grain, approved joins, synonyms, default time windows, freshness and quality indicators, lineage and security filters. A wide view can pre-join common dimensions for an agent, while the underlying gold facts and dimensions remain the durable source for other workloads.
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Why is grain the first contract for an AI model?
Grain states what one row represents. Write it as one sentence before creating a fact table, such as “one row per order line per fulfillment event.” Without that sentence, an agent can join two tables at incompatible levels, count duplicated rows or apply a measure to the wrong time period.
Make grain visible in every interface
Put the grain in the table description and in the semantic catalog, not only in a design document. Name objects so their level is apparent: order_line_fulfillment_fact is safer than a generic orders table when multiple events exist. If a view deliberately changes the grain, document the new level and the aggregation performed.
Classify every measure
Mark measures as additive, semi-additive or non-additive and state the valid aggregation and time context. Revenue may be additive across orders and products but not across snapshots; an account balance is usually additive across accounts but not across time; a conversion rate must be calculated from its numerator and denominator rather than averaged. Publish these rules as semantic metadata and enforce them in reusable calculations.
How should facts, dimensions and keys be modeled?
A star schema is the safest default for structured analytics: fact tables store measurements at a declared grain, while dimension tables describe entities such as date, customer, product, location and channel. Microsoft’s semantic-model guidance recommends lean models with one-column, one-to-many relationships and explicit measures because this reduces ambiguous paths.
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Use conformed dimensions
A conformed date or customer dimension has the same meaning wherever it is used. Shared dimensions let an assistant answer questions that cross processes, such as comparing orders with support cases by month, without inventing a different definition of customer in each mart. Keep relationship direction simple and avoid many-to-many joins unless the bridge table and its counting behavior are explicitly modeled.
Use durable surrogate keys
Assign warehouse surrogate keys and retain a mapping to each source system’s natural key. Do not regenerate keys during a full refresh when facts depend on them; otherwise historical joins can silently change. Keep rejected or unresolved keys visible in quality reporting so an agent does not treat an unknown entity as a real customer or product.
Preserve history deliberately
For changing dimensions, store effective-start and effective-end timestamps and a current-row indicator when the business needs reproducible historical reports. A query for sales “by territory in March” should use the territory valid in March, while a current account list may use the current row. Expose both interpretations with distinct measures or filters instead of making the agent choose.
Resolve identity across systems
If billing, CRM and support systems use different customer IDs or disagree about active status, publish a governed golden record and document the survivorship rule. Include source memberships and confidence or exception flags where identity is not certain. A semantic synonym such as “client” can point to this canonical entity rather than to whichever source table happens to contain that word.
Which warehouse interface should an AI agent use?
No single shape fits every task. Compare the options by semantic clarity, grain, performance, history, discoverability, security and maintenance rather than by novelty.
| Interface | Best use | Strengths | Risks and controls |
|---|---|---|---|
| Star-schema semantic model | Governed metrics and recurring analytics | Clear grain, conformed dimensions, reusable measures and strong historical behavior | Requires careful modeling; keep relationships simple and publish explicit calculations |
| Wide AI-facing view | Frequent natural-language questions over a single domain | Fewer joins for the agent, friendly labels and predictable query patterns | Can hide grain changes or become expensive; document every pre-join and keep the gold model underneath |
| Normalized warehouse model | Operational integration and detailed audit trails | Less duplication and flexible source representation | Join paths are harder for an LLM to infer; expose curated marts rather than raw normalized tables |
| Graph or retrieval layer | Relationships, documents and contextual explanations | Can connect entities, policies and unstructured evidence with provenance | Not a replacement for governed metric arithmetic; map graph facts to warehouse measures and retain source citations |
For most natural-language reporting, use the star-schema semantic model as the metric authority and add a wide view only where it removes a repeated, well-understood join. Add a graph or retrieval layer when questions require documents, policies or relationship paths that are not naturally represented as measures.
What metadata does a semantic layer need for AI?
A conventional semantic model supplies names, descriptions, relationships, calculations, time logic, aggregation behavior and row-level security. An AI-facing model needs additional context that helps an agent select and explain the right object.
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- Canonical metrics: Store one formula, owner and revision history for each metric. Include synonyms such as “sales” and “net revenue” only when they intentionally resolve to the same definition.
- Grain and business meaning: Describe what each table row and column represents, including units, currency, timezone and whether a value is current-state or event-time.
- Allowed filters and defaults: State valid dimensions, default time windows, excluded test records and whether null means unknown, not applicable or zero.
- Examples: Provide approved natural-language questions and the expected SQL or calculation pattern. Examples should demonstrate correct joins and denominator handling, not merely show syntax.
- Freshness and quality: Publish the last successful load, expected refresh interval, validation status and known exceptions so an answer can disclose stale data.
- Lineage and provenance: Link a metric to its transformation, source systems and, where possible, the record or document used to support an answer.
- Permissions: Carry warehouse or catalog permissions into the semantic interface, including row-level filters and sensitive-column masking.
How do you stop an AI assistant from making up metrics?
Prevent fabrication by making the semantic interface the only metric authority, validating generated queries and returning evidence with every result. Prompting alone cannot repair ambiguous grain or conflicting definitions.
Expose measures instead of raw arithmetic
Define measures such as net_revenue, active_customers and retention_rate once. A measure definition should specify included statuses, refunds, currency conversion, denominator, date field and valid aggregation. Hide or mark raw columns that should never be summed directly.
Constrain query generation
Give the agent an allow-list of semantic objects and joins. Reject SQL that references undocumented tables, bypasses a security predicate, mixes incompatible grains or aggregates a non-additive measure incorrectly. A dry-run validator can inspect the query plan before execution and return a precise correction to the agent.
Return an explanation packet
Alongside a number, return the metric name and version, filters, time range, source freshness, row-level policy applied and lineage links. If the question is ambiguous—such as “customers” meaning billed accounts or active users—ask a clarification question or present the available definitions rather than selecting silently.
Evaluate against a fixed question set
Maintain representative questions with expected totals, filters and citations. Test exact values, edge cases such as refunds and daylight-saving boundaries, unauthorized requests, stale partitions and questions that should be refused because no governed metric exists.
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How should warehouse data be governed before connecting an LLM?
Treat each curated domain as a data product with an owner, contract, lifecycle and service expectations. Databricks guidance emphasizes semantic consistency, layered quality improvements, data contracts, controlled schema evolution, catalogs, precise lineage and first-class access control.
Assign ownership and a change process
Every metric, fact and dimension needs a business owner and a technical steward. A contract should state schema, grain, freshness target, quality checks, permitted breaking changes and deprecation dates. Version metric definitions and notify consumers before changing a formula or historical interpretation.
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Register assets in a catalog
A catalog should make descriptions, classifications, owners and lineage searchable. Unity Catalog is documented as a unified governance layer for data and AI that controls access, discovers assets, tracks lineage, classifies sensitive data and monitors quality. Whatever catalog you use, ensure the AI interface inherits the same policies as interactive BI.
Control schema evolution
Adding a nullable descriptive column may be compatible; changing a key, grain, unit or metric formula is not. Use automated contract checks in ingestion and transformation pipelines, quarantine incompatible records and publish migration notes. Never let an application rename a column and leave the semantic catalog unchanged.
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Apply authorization before query execution and preserve time-aware joins. Row-level security should filter the fact rows a user may see, while column policies mask or remove sensitive attributes. Test indirect leakage: an agent should not reveal a restricted total by subtracting two permitted totals or by using an unprotected aggregate.
Historical accuracy depends on effective dates, durable keys and an explicit choice of event date versus current dimension value. Carry those rules into semantic calculations and prohibit agents from joining a current snapshot to historical facts unless that behavior is intentionally named and documented.
How do you build the model step by step?
- Inventory questions and owners. List the questions the assistant must answer, identify the business owner for each metric and record the acceptable source and citation.
- Select one domain and declare grain. Write the grain sentence for every fact, identify dimensions and decide which measures are additive, semi-additive or non-additive.
- Build replayable bronze, silver and gold paths. Preserve source records and ingestion metadata, then add type checks, deduplication, standardization, identity resolution, history handling and quality tests.
- Add conformed dimensions and durable keys. Create shared date, customer, product, location and channel dimensions where appropriate; retain source-key mappings and unknown-member handling.
- Publish canonical measures. Implement formulas, time behavior, units, currency rules and exclusions once in the gold or semantic layer.
- Create the narrow AI surface. Add descriptions, synonyms, examples, approved joins, default filters, freshness, quality status, lineage, provenance and security predicates. Pre-join only relationships whose grain is unambiguous.
- Register and protect assets. Add catalog ownership, sensitive-data classifications, row-level policies, schema contracts and controlled evolution rules.
- Evaluate before expansion. Run the fixed question set for correctness, permissions, latency, cost and citation quality. Expand to the next domain only after failures have owners and fixes.
What should you test before allowing production questions?
Test the complete path, not just SQL execution. A query can return a plausible number while violating the business definition or a user’s permissions.
- Semantic correctness: Compare generated answers with approved results for totals, ratios, distinct counts and time comparisons.
- Grain safety: Include many-to-one, one-to-many, bridge-table and late-arriving-dimension cases to detect double counting.
- Temporal behavior: Test fiscal calendars, timezone boundaries, slowly changing dimensions, restatements and incomplete current periods.
- Security: Verify row and column policies for every role, including attempts to infer restricted values through aggregates.
- Freshness and failure: Simulate a late load, failed quality check or stale partition and ensure the assistant warns or refuses instead of presenting old data as current.
- Performance and cost: Measure latency and resource use for common and worst-case prompts; cap scans and require aggregation for large fact tables.
- Evidence quality: Check that answers identify the metric version, filters, freshness and lineage needed for a reviewer to reproduce them.
When is a graph or retrieval layer necessary?
Use the warehouse semantic layer for governed measures, dimensions and calculations. Add a graph or retrieval layer when the answer depends on documents, policies, equipment relationships or other context that is not a stable numeric measure.
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What does a practical model look like?
Suppose the business asks, “What was net revenue by region for customers acquired last quarter?” A robust design might use an order-line fact at one row per order line, a payment or refund fact at its own grain, a customer dimension with acquisition date and effective history, and conformed date and region dimensions. The semantic layer defines net revenue as approved sales minus eligible refunds, states which order date is used, and exposes a customer-acquisition filter that joins through the historical customer key.
The AI-facing view can present region, acquisition-quarter and the canonical net-revenue measure without exposing every operational column. Its metadata should show the grain, permitted joins, currency and freshness. If the user asks for “customers” without specifying acquired, billed or active, the assistant should ask which governed definition to use.
What are the common modeling failures and their fixes?
- Raw-table access: Remove direct access and publish curated objects with approved joins.
- Mixed-grain fact: Split orders, shipments, payments and snapshots into separate facts; relate them through conformed dimensions or documented bridges.
- Metric drift: Centralize formulas, assign owners and version changes instead of allowing each prompt or dashboard to calculate its own variant.
- Current values replacing history: Add effective dates, current-row indicators and durable keys; test reports for past periods.
- Ambiguous identity: Maintain a golden record and source mappings, with exceptions visible to data stewards.
- Unprotected semantic views: Apply inherited row-level security and sensitive-field classification, then test inference attacks.
- Undocumented freshness: Publish load timestamps and quality status and make stale data trigger a warning or refusal.
- Uncontrolled schema changes: Enforce data contracts and compatibility checks before deploying transformations.
Frequently Asked Questions
Can an LLM safely query bronze or raw warehouse tables?
Not as a default. Raw tables usually lack declared grain, stable names, conformed keys and business definitions, so expose curated semantic objects and reserve raw access for tightly controlled engineering workflows.
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Should every metric have its own table?
No. Store metrics in a shared semantic model or metric layer when they use common facts and dimensions; create a separate aggregate only when its grain, refresh behavior or performance purpose is materially different.
How can I handle a business term that has two legitimate definitions?
Publish both as separately named governed metrics, include the distinguishing rule in each description and require the assistant to ask a clarification question when the user’s wording does not select one.
What should happen when a source system is late or fails quality checks?
Mark the affected asset as stale or unhealthy in catalog metadata, block or qualify answers that depend on it and retain the last successful load timestamp so users can judge whether the result is usable.
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