Examples

For a detailed, hands-on introduction to the project, please see our quickstart notebooks. They provide a complete walkthrough of the library's capabilities using real-world datasets.

Domain	Notebook	Open in Colab
Healthcare	quickstart_healthcare.ipynb
Tech Manufacturing	quickstart_tech_manufacturing.ipynb
FMCG	quickstart_fmcg.ipynb
Sports Media	quickstart_sports_media.ipynb
Conceptual Search	quickstart_conceptual_search.ipynb
Databricks Unity Catalog [Health Care]	quickstart_healthcare_databricks.ipynb	Databricks Notebook Only
Snowflake Horizon Catalog [ FMCG ]	quickstart_fmcg_snowflake.ipynb	Snowflake Notebook Only
Native Snowflake with Cortex Analyst [ Tech Manufacturing ]	quickstart_native_snowflake.ipynb
Native Databricks with AI/BI Genie [ Tech Manufacturing ]	quickstart_native_databricks.ipynb
Streamlit App	quickstart_streamlit.ipynb

These datasets will take you through the following steps:

• Generate Semantic Model → The unified layer that transforms fragmented datasets, creating the foundation for connected intelligence.
  • 1.1 Profile and classify data → Analyze your data sources to understand their structure, data types, and other characteristics.
  • 1.2 Discover links & relationships among data → Reveal meaningful connections (PK & FK) across fragmented tables.
  • 1.3 Generate a business glossary → Create business-friendly terms and use them to query data with context.
  • 1.4 Enable semantic search → Intelligent search that understands meaning, not just keywords—making data more accessible across both technical and business users.
  • 1.5 Visualize semantic model→ Get access to enriched metadata of the semantic layer in the form of YAML files and visualize in the form of graph
• Build Unified Data Products → Simply pick the attributes across your data tables, and let the toolkit auto-generate queries with all the required joins, transformations, and aggregations using the semantic layer. When executed, these queries produce reusable data products.