Semantic Layer Builder

Build enterprise-grade semantic models with metrics, dimensions, and business context

12 Active Agents
1
Domain
Select data domain
2
Use Cases
Business questions
3
Dimensions
Define hierarchies
4
Metrics
Define KPIs & measures
5
Relationships
Entity connections
6
Semantic Model
Virtual entities
7
Quality & Governance
Rules & access
8
Review & Deploy
Finalize & publish

Select Your Domain Configuration

Choose a saved domain configuration to build your data model from

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Create New Domain Config

What Business Questions Do You Want to Answer?

Select the type of analytics and define the questions your data model should answer

Select Analytics Categories (Select one or more to generate questions)

Business Analytics

Trends, patterns, KPIs, and performance metrics

Click to select

Operational Reporting

Daily reports, compliance, and operational insights

Click to select

ML Models

Churn, forecasting, segmentation, recommendations

Click to select

Operational Intelligence

Real-time monitoring, alerts, and actions

Click to select

Business Questions

Select one or more categories above to generate business questions

Define Dimensions & Hierarchies

Create dimension tables with drill-down hierarchies for analysis

Dimension Tables

Define dimension tables for slicing and filtering your data

Dimension Hierarchies

Define drill-down paths for each dimension (e.g., Year → Quarter → Month → Day)

Fact Tables

Define fact tables containing measures and foreign keys to dimensions

Fact Table Grain

Select the atomic level of detail for this fact table

Transaction Level
Daily Snapshot
Monthly Aggregate
Customer Level
Product Level

Join Path Validation

Verify join paths and detect potential fan-out issues that cause double-counting

Fan-out Detection
The following join paths may cause measure duplication (fan-out):
fact_sales
N:M
bridge_promo
M:1
dim_promotion
Suggestion: Use DISTINCT aggregation or define a bridge table allocation factor

Valid Join Paths

fact_sales
N:1
dim_customer
Valid
fact_sales
N:1
dim_product
Valid
fact_sales
N:1
dim_date
Valid

Materialization & Caching

Configure pre-aggregations and caching strategies for optimal query performance

Pre-Aggregation Strategy

Full Refresh
Rebuild entire aggregate on schedule
Incremental
Only process new/changed data
LIVE
Real-time Streaming
Continuous micro-batch updates

Refresh Schedule

Hourly
Daily
Weekly
Manual

Cache Configuration

Pre-Aggregation Tables

daily_sales_summary GROUP BY: date, product_category ~100x faster
monthly_customer_metrics GROUP BY: month, customer_segment ~50x faster

Define Metrics & Measures

Create business metrics, KPIs, and calculated measures for your semantic layer

Base Measures

Define quantitative facts that can be aggregated (sum, count, average, min, max)

Key Performance Indicators

Define business KPIs that combine measures with targets and thresholds

Calculated Metrics

Define derived metrics using formulas and existing measures

Time Intelligence

Define time-based calculations like YTD, MTD, same period last year, etc.

Data Contracts

Define data quality rules and contracts for your semantic model

Define Entity Relationships

Map relationships between fact tables and dimensions

Entity Relationship Diagram

Click "Generate with AI" to automatically create relationships
based on your dimensions and fact tables

Detected Entities from Previous Steps

FACT TABLES
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DIMENSION TABLES
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Defined Relationships

0 relationships

No relationships defined yet.
Click "Generate with AI" to automatically create relationships.

Cardinality Types

1:1 One to One 1:N One to Many (Star Schema) N:M Many to Many (Bridge Table) 0:N Zero or Many (Optional)

Build Semantic Model

Create virtual entities that abstract the physical model for business users

Semantic Entities

Semantic entities are business-friendly views that combine and abstract physical tables

Semantic Attributes

Define business-friendly names, descriptions, and formatting for attributes

Data Lineage

Visualize data flow from sources through transformations to semantic layer

Source Tables
Raw data
Transformations
ETL/ELT
Semantic Layer
Business entities
Reports & Dashboards
Consumer apps

Impact Analysis

Select an entity to see downstream impact of changes

12
Dependent Reports
5
ML Models
28
Affected Users

Physical Table Mapping

Map semantic entities to physical tables and datasets

Quality & Governance

Define data quality rules, certification levels, and access controls

Data Quality Rules

98%
Completeness
Non-null values
92%
Accuracy
Correct values
99%
Consistency
Cross-source match
78%
Timeliness
Data freshness
100%
Uniqueness
No duplicates
95%
Conformity
Format compliance

Data Certification

Gold Highest trust level

Production-ready, fully documented, meets all quality thresholds, executive approved

Silver Trusted

Quality verified, documented, approved by data steward

Bronze Basic

Initial certification, meets minimum quality standards

Access Control

Public All users
Restricted Specific roles
Confidential Approved only

Row-Level Security

Define filters that restrict data visibility based on user attributes

Data Ownership

Data Owner

Business owner responsible for data accuracy

Data Steward

Technical steward managing data quality

Support Contact

Contact for questions about this data

Review & Deploy Semantic Layer

Review your complete semantic model and deploy to production

AI Model Review

LLM-powered analysis of your semantic layer

Semantic Layer Summary

0
Use Cases
0
Metrics
0
Dimensions
0
Fact Tables
0
Relationships
0
Semantic Entities
0
Quality Rules
0
Certifications

Metrics & KPIs

0 items

Dimensions & Hierarchies

0 items

Semantic Entities

0 items

Quality & Governance

0 items

Deployment Configuration

Export Formats

Existing Inventory

Review existing ML models, datasets, and feature definitions before creating tasks

ML Models
Datasets
Features
Total Records
ML Model Registry

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Dataset Registry

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Feature Store

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PostgreSQL Scripts

Generate DDL scripts for data model and BI layer physicalization

Data Model DDL

Dimensions, Fact Tables, Relationships

Not Generated
-- Click "Generate" to create PostgreSQL DDL -- Includes: CREATE TABLE, PRIMARY KEYS, FOREIGN KEYS -- Based on your dimensions and fact tables

BI Model DDL

Measures, KPIs, Calculated Fields, Views

Not Generated
-- Click "Generate" to create PostgreSQL DDL -- Includes: VIEWS, MATERIALIZED VIEWS, FUNCTIONS -- Based on your measures, KPIs, and calculations

Implementation Tasks

Create and assign tasks for ML models, datasets, reports, and nudge templates

0
Total Tasks
0
ML Models
0
Datasets
0
Reports
0
Nudge Templates
ML Model
Dataset
Data Pipeline
Feature Eng.
Report
Dashboard
Workflow
Nudge Template

No tasks created yet. Click buttons above to create tasks.

Version Control

Track changes and maintain history

Current Branch
main
Last Commit
Initial setup
Pending Changes
3 modifications

Testing & Validation

Run tests to validate semantic layer integrity

12
Tests Passed
0
Tests Failed
2
Warnings
100%
Coverage

API & SDK Generation

Generate REST APIs and client SDKs for your semantic layer

Python SDK
JavaScript SDK
Java SDK
GraphQL

Semantic Search & Discovery

Search across all metrics, dimensions, and entities

Popular Metrics

Total Revenue Customer Count Avg Order Value

Recently Viewed

dim_customer fact_sales