Semantic Layer API Design: Building Interfaces for Governed Metrics

Semantic layer API design is the practice of creating programmatic interfaces that expose governed metrics to applications, AI systems, and analytics tools. A well-designed API makes the semantic layer accessible beyond traditional BI tools, enabling embedded analytics, custom applications, and intelligent systems to consume consistent, trusted metrics.

The API is how the semantic layer's value reaches the broader technology ecosystem. Poor API design limits adoption; excellent API design makes governed metrics the natural choice for every data need.

Core API Design Principles

Principle 1: Metric-Centric, Not Table-Centric

Traditional data APIs expose tables and columns. Semantic layer APIs expose business concepts:

Table-centric (avoid):

GET /tables/orders/query?columns=amount,status&filter=status=complete

Metric-centric (preferred):

GET /metrics/revenue?dimensions=region,quarter

The API should speak business language, hiding technical implementation.

Principle 2: Discoverability

Clients should discover available metrics programmatically:

GET /metrics
{
  "metrics": [
    {
      "name": "revenue",
      "description": "Total recognized revenue from completed orders",
      "dimensions": ["region", "product", "customer_segment"],
      "certified": true
    },
    {
      "name": "customer_count",
      "description": "Count of unique active customers",
      "dimensions": ["region", "acquisition_channel"],
      "certified": true
    }
  ]
}

Discovery enables dynamic interfaces and AI integration.

Principle 3: Explicit Context

Every query should clearly specify what is being requested:

POST /query
{
  "metrics": ["revenue", "order_count"],
  "dimensions": ["region", "month"],
  "filters": [
    {"dimension": "month", "operator": ">=", "value": "2024-01"}
  ],
  "sort": [{"dimension": "month", "direction": "asc"}],
  "limit": 100
}

No implicit defaults that cause confusion.

API Patterns

Pattern 1: Query API

Clients construct queries specifying metrics, dimensions, and filters:

Request:

POST /v1/query
{
  "metrics": ["revenue"],
  "dimensions": ["product_category"],
  "filters": [
    {"field": "region", "operator": "=", "value": "North America"}
  ]
}

Response:

{
  "columns": ["product_category", "revenue"],
  "rows": [
    ["Electronics", 1250000],
    ["Software", 890000],
    ["Services", 445000]
  ],
  "metadata": {
    "query_id": "q-123456",
    "execution_time_ms": 245,
    "cache_hit": false
  }
}

This is the most flexible pattern.

Pattern 2: Pre-built Metric Endpoints

Dedicated endpoints for specific metrics:

GET /v1/metrics/revenue?region=North America&group_by=product_category
GET /v1/metrics/customer_count?segment=enterprise
GET /v1/metrics/mrr?as_of=2024-01-31

Simpler for clients but less flexible.

Pattern 3: SQL Generation API

Return generated SQL rather than executed results:

Request:

POST /v1/generate-sql
{
  "metrics": ["revenue"],
  "dimensions": ["region"]
}

Response:

{
  "sql": "SELECT region, SUM(amount) as revenue FROM orders WHERE status = 'complete' GROUP BY region",
  "warehouse": "snowflake_production"
}

Useful when clients need to execute queries themselves.

Pattern 4: GraphQL Interface

GraphQL for flexible metric querying:

query {
  metrics(names: ["revenue", "order_count"]) {
    data(dimensions: ["region"], filters: [{field: "year", value: "2024"}]) {
      dimensions
      values
    }
    metadata {
      certified
      lastUpdated
    }
  }
}

Good for clients wanting precise data fetching.

API Components

Metric Catalog Endpoints

Enable discovery of available metrics:

GET /v1/metrics                    # List all metrics
GET /v1/metrics/{name}             # Get metric details
GET /v1/metrics/{name}/dimensions  # Get valid dimensions
GET /v1/dimensions                 # List all dimensions
GET /v1/dimensions/{name}/values   # Get dimension values

Query Execution Endpoints

Execute metric queries:

POST /v1/query              # Execute a query
GET  /v1/query/{id}         # Get query status/results
POST /v1/query/validate     # Validate without executing
POST /v1/query/explain      # Get execution plan

Administration Endpoints

Manage the semantic layer:

GET  /v1/admin/cache/stats      # Cache statistics
POST /v1/admin/cache/clear      # Clear cache
GET  /v1/admin/connections      # Data source status
GET  /v1/admin/health           # Health check

Authentication and Authorization

Authentication Patterns

API Keys:

Authorization: Bearer sk_live_abc123def456

Simple, suitable for server-to-server.

OAuth 2.0 / JWT:

Authorization: Bearer eyJhbGciOiJIUzI1NiIs...

User-context aware, supports fine-grained permissions.

Service Accounts: For system integrations with elevated access.

Authorization Model

Implement granular permissions:

role: analyst
permissions:
  metrics:
    - revenue: read
    - customer_count: read
    - cost_breakdown: denied
  dimensions:
    - region: read
    - employee_name: denied
  row_access:
    region: ["North America", "Europe"]

The API should enforce these permissions on every request.

Response Design

Successful Responses

Consistent structure for all successful queries:

{
  "data": {
    "columns": ["region", "revenue"],
    "rows": [
      ["North America", 5000000],
      ["Europe", 3200000]
    ]
  },
  "metadata": {
    "query_id": "q-789",
    "metrics_used": ["revenue"],
    "dimensions_used": ["region"],
    "filters_applied": [],
    "row_count": 2,
    "execution_time_ms": 156,
    "cache_hit": true,
    "data_freshness": "2024-02-17T10:30:00Z"
  }
}

Error Responses

Clear, actionable error information:

{
  "error": {
    "code": "INVALID_DIMENSION",
    "message": "Dimension 'city' is not valid for metric 'revenue'",
    "details": {
      "metric": "revenue",
      "requested_dimension": "city",
      "valid_dimensions": ["region", "country", "product_category"]
    },
    "documentation_url": "https://docs.example.com/errors/INVALID_DIMENSION"
  },
  "request_id": "req-456"
}

Pagination

For large result sets:

{
  "data": { ... },
  "pagination": {
    "total_rows": 10000,
    "returned_rows": 100,
    "offset": 0,
    "limit": 100,
    "next_cursor": "eyJvZmZzZXQiOjEwMH0="
  }
}

Performance Considerations

Caching Headers

Use HTTP caching appropriately:

Cache-Control: max-age=300, private
ETag: "abc123"
Last-Modified: Sun, 17 Feb 2024 10:30:00 GMT

Async Queries

For long-running queries:

POST /v1/query
{
  "async": true,
  ...
}

Response:
{
  "query_id": "q-999",
  "status": "running",
  "poll_url": "/v1/query/q-999"
}

Rate Limiting

Protect the system and ensure fairness:

X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 995
X-RateLimit-Reset: 1708172400

AI and LLM Integration

Semantic Context Endpoints

Provide context for AI systems:

GET /v1/context/metrics
{
  "metrics": [
    {
      "name": "revenue",
      "description": "Total recognized revenue...",
      "calculation_description": "Sum of order amounts where status is complete",
      "business_context": "This is the primary top-line metric used for financial reporting",
      "common_questions": [
        "What is our total revenue?",
        "How does revenue compare by region?"
      ]
    }
  ]
}

Natural Language Endpoint

Accept natural language queries:

POST /v1/query/natural-language
{
  "question": "What was our revenue in Q4 by region?",
  "context": {
    "user_department": "finance",
    "preferred_metrics": ["revenue"]
  }
}

Best Practices

Versioning

• Use explicit version prefixes: /v1/, /v2/
• Maintain backward compatibility within major versions
• Provide migration guides between versions
• Deprecate gracefully with advance notice

Documentation

• OpenAPI/Swagger specifications
• Interactive API explorer
• Code examples in multiple languages
• Clear error documentation

Monitoring

• Track API usage by endpoint and client
• Monitor latency percentiles
• Alert on error rate spikes
• Log queries for debugging

Security

• Always use HTTPS
• Validate and sanitize all inputs
• Implement request signing for sensitive operations
• Audit access to governed metrics

A well-designed semantic layer API transforms governed metrics from a BI tool feature into an enterprise capability accessible to any system that can make HTTP requests. The API is the semantic layer's front door - make it welcoming, secure, and reliable.