Semantic Layer

Every Enrerprise
Needs a Semantic Layer

Synmetrix's semantic layer acts as a user-friendly, API-first bridge between data sources and applications. It ensures data consistency, security, speed, and accessibility, empowering data professionals for seamless application integration.

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What is a
Semantic Layer?

A mediator translating raw data for users, the Semantic Layer simplifies communication
between sources and applications.

The Problem

Data chaos due to scattered
models and decentralized control

The Solution

Centralize control with a semantic layer
for trust and consistency

The Parts of a Complete, Universal Semantic Layer

Data Modeling

Defines metrics and data models for better apps insights.

SQL API

Ensures different systems can work together smoothly

Access Control

Ensures safe access to the right data for users

Caching

Stores data for faster handling of multiple requests

See All Features

Key Benefits

Made for enterprises

Single sourse of truth

On-premise Deployment

Improve data governance

Acceleration in BI Query Speed

Reduced Time to Insight

Reduction in Compute Consumption

Improved Resource Productivity

Usage Examples

    cubes:
  - name: users
    sql_table: users
 
    measures:
      - name: count
        sql: id
        type: count
 
    dimensions:
      - name: city
        sql: city
        type: string
 
      - name: company_name
        sql: company_name
        type: string
    
  

    cube(`users`, {
  sql_table: `users`,
 
  measures: {
    count: {
      sql: `id`,
      type: `count`,
    },
  },
 
  dimensions: {
    city: {
      sql: `city`,
      type: `string`,
    },
 
    company_name: {
      sql: `company_name`,
      type: `string`,
    },
  },
});
    
  

More in Docs

    cubes:
  - name: users
    sql_table: users
 
    measures:
      - name: count
        sql: id
        type: count
 
    dimensions:
      - name: city
        sql: city
        type: string
 
      - name: company_name
        sql: company_name
        type: string
    
  

    cube(`users`, {
  sql_table: `users`,
 
  measures: {
    count: {
      sql: `id`,
      type: `count`,
    },
  },
 
  dimensions: {
    city: {
      sql: `city`,
      type: `string`,
    },
 
    company_name: {
      sql: `company_name`,
      type: `string`,
    },
  },
});
    
  

See full info in Docs

    cubes:
  - name: users
 
    measures:
      - name: count
        sql: id
        type: count
 
      - name: paying_count
        sql: id
        type: count
        filters:
          - sql: "{CUBE}.paying = 'true'"
 
      - name: paying_percentage
        sql: "100.0 * {paying_count} / {count}"
        type: number
        format: percent
 
    # ...
    
  

    cube(`users`, {
  measures: {
    count: {
      sql: `id`,
      type: `count`,
    },
 
    paying_count: {
      sql: `id`,
      type: `count`,
      filters: [{ sql: `${CUBE}.paying = 'true'` }],
    },
 
    paying_percentage: {
      sql: `100.0 * ${paying_count} / ${count}`,
      type: `number`,
      format: `percent`,
    },
  },
 
  // ...
});
    
  

See full info in Docs

    cubes:
  - name: orders
    sql_table: orders
 
    measures:
      - name: count
        type: count
 
    dimensions:
      - name: id
        sql: id
        type: number
        primary_key: true
 
      - name: status
        sql: status
        type: string
 
      - name: completed_at
        sql: completed_at
        type: time
    
  

    cube(`orders`, {
  sql_table: `orders`,
 
  measures: {
    count: {
      type: `count`,
    },
  },
 
  dimensions: {
    id: {
      sql: `id`,
      type: `number`,
      primary_key: true,
    },
 
    status: {
      sql: `status`,
      type: `string`,
    },
 
    completed_at: {
      sql: `completed_at`,
      type: `time`,
    },
  },
});
    
  

See full info in Docs

    cubes:
  - name: orders
    sql_table: orders
 
    measures:
      - name: count
        type: count
 
    dimensions:
      - name: status
        type: string
        sql: status
 
      - name: created_at
        type: time
        sql: created_at
    
  

    cube(`orders`, {
  sql_table: `orders`,
 
  measures: {
    count: {
      type: `count`,
    },
  },
 
  dimensions: {
    status: {
      sql: `status`,
      type: `string`,
    },
 
    created_at: {
      sql: `created_at`,
      type: `time`,
    },
  },
});
    
  

It would be represented as table in SQL API with count, status, created columns.
To get the count of orders grouped by status we can run the following query.

    cube=> SELECT count, status FROM orders;
 count |   status
-------+------------
 15513 | completed
 14652 | processing
 13829 | shipped
(3 rows)
    
  

See full info in Docs