Connect Multiple Data Sources → Create Unified Schema → Enable AI Access
Integrate scattered enterprise data sources into a single, AI-ready data layer that multiple teams can access through a consistent API.
Workflow Steps
Airbyte
Extract data from multiple sources
Set up Airbyte connectors to pull data from your CRM (Salesforce), database (PostgreSQL), marketing tools (HubSpot), and file storage (S3). Configure incremental sync schedules to keep data fresh.
Snowflake
Load raw data into data warehouse
Configure Airbyte to load all extracted data into Snowflake's raw data layer. Set up separate schemas for each source system to maintain data lineage and enable easy debugging.
dbt
Transform data into unified business models
Create dbt models that join and transform raw data into business-ready tables. Build a unified customer view, standardized metrics, and AI-friendly feature tables. Include data quality tests in your transformations.
dbt
Generate data catalog and lineage
Use dbt docs to automatically generate documentation showing data lineage, model descriptions, and column definitions. This creates a self-service catalog that AI teams can use to understand available data.
Workflow Flow
Step 1
Airbyte
Extract data from multiple sources
Step 2
Snowflake
Load raw data into data warehouse
Step 3
dbt
Transform data into unified business models
Step 4
dbt
Generate data catalog and lineage
Why This Works
This modern ELT stack solves the data fragmentation problem by creating a single source of truth with proper documentation, making it easy for AI teams to discover and use enterprise data.
Best For
Enterprise teams consolidating data silos for AI projects
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