Define your data once.

Trust it everywhere.

The Enterprise Context Layer turns scattered data into governed, versioned Data Products: business definitions, access policy, and lineage built in. One source of truth for analysts, apps, and AI.
The Starburst enterprise context layer

The problem

Three tools. Three answers. Zero trust.

Every BI tool, every Python notebook, every AI agent calculates metrics differently. Avoid this by defining your context in a certified Data Product.
Tableau
Monthly Recurring Revenue$4.2M
Power BI
Monthly Recurring Revenue$4.6M
Python
Monthly Recurring Revenue$3.9M
revenue_metrics data product · v1.3 · certified$4.4M

Data Products

Raw tables become trusted Data Products

A Data Product is a governed dataset bundled with its business definitions, access policies, ownership, and lineage, treated like a product, not a one-off query.

  • Discoverable in a shared catalog; any team can find and trust it
  • Policy travels with the product: Role-based Access Control (RBAC), column masking, row-level security
  • Versioned and tested: breaking changes reviewed before rollout
  • Consumed by BI tools, APIs, and AI agents: same definition, every time
Data Product summary card for "Customer 360" (customer_360), marked certified: unified customer view across CRM, transactions, and support; owned by data-platform and refreshed hourly; datasets customers (Iceberg MV, PII masked), order_summary and support_health (Iceberg Views); tagged customer, pii-masked, ai-ready.

Data Products as code

Your business definitions. In git. In CI/CD.

Data Products can now be defined in YAML. Commit to git, test before merge, roll back when needed.

customer_360.sql
-- customer_360.sql
-- Increment version → triggers review workflow in CI

CREATE OR REPLACE DATA PRODUCT customer_360
  VERSION 2
  DESCRIPTION 'Unified customer view: CRM + orders + support'
  OWNER 'data-platform@acme.com'
  TAGS ('certified', 'customer', 'pii-masked')
  REFRESH SCHEDULE '0 * * * *'          -- hourly

DATASET customers
  MATERIALIZED                               -- Iceberg MV for performance
  DESCRIPTION 'One row per customer, masked PII'
AS
  SELECT
    c.customer_id,
    c.name,
    sha256(c.email)           AS email_hash,
    COUNT(o.order_id)         AS total_orders,
    SUM(o.amount)             AS lifetime_value,    -- canonical definition
    c.segment,
    c.region,
    MAX(s.health_score)       AS support_health
  FROM      crm.customers  c
  LEFT JOIN orders.line_items o ON c.customer_id = o.customer_id
  LEFT JOIN support.scores   s ON c.customer_id = s.customer_id
  GROUP BY 1, 2, 3, 6, 7;

Version control: Commit Data Product definitions to git alongside application code. Diff, review, merge.

Policy as code: Access rules live in the same repo as the definition. No separate admin console to remember.

CI/CD lineage checks: Validate upstream freshness automatically before publishing a new version.

The semantic layer

Your AI has no context. Data Products fix that.

The semantic layer translates raw data into business-ready context: metrics, dimensions, and relationships your tools and AI agents can trust. Data Products are that context layer, built into the query platform itself.

LLMs struggle with raw database schemas. They excel when given structured business context. Context, gained through the semantic layer, is what closes the gap between AI potential and AI accuracy.Starburst, What is the Semantic Layer?
Amazon S3 / Iceberg
Snowflake
PostgreSQL
+47 more
Enterprise Context Layer

Governed data products · Versioned · AI-ready

customer_360 v2revenue_metrics v1fraud_signals v3
BI tools (Tableau, Power BI)
AIDA (natural language)
AI agents (MCP / APIs)
Apps (JDBC / REST)

See what changes when intelligence reaches all your data.

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