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Supported Databases

dazense supports all major data warehouses and databases:
  • Snowflake
  • BigQuery
  • Databricks
  • PostgreSQL
  • Redshift
  • MySQL
  • SQL Server
  • And more…

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Adding a Database

During Initialization When you run dazense init, you can add a database connection interactively: 
dazense init
Manual Configuration Add database connections in your dazense_config.yaml: 
databases:
  - name: production_warehouse
    type: snowflake
    host: account.snowflakecomputing.com
    database: analytics
    schema: public
    user: ${SNOWFLAKE_USER}
    password: ${SNOWFLAKE_PASSWORD}
    warehouse: compute_wh

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Database Setup

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Snowflake

databases:
  - name: snowflake_prod
    type: snowflake
    host: account.region.snowflakecomputing.com
    database: analytics
    schema: public
    user: ${SNOWFLAKE_USER}
    password: ${SNOWFLAKE_PASSWORD}
    warehouse: compute_wh
    role: analyst_role

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BigQuery

databases:
  - name: bigquery_prod
    type: bigquery
    project_id: my-project
    dataset_id: analytics
    credentials_path: /path/to/service-account.json
    accessors:
      - columns
      - preview
      - description
      - profiling
    include: []
    exclude: []
    sso: false
    location: null
The service account should have BigQuery User role at minimum to read schemas and query tables.

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Databricks

databases:
  - name: databricks_prod
    type: databricks
    host: dbc-12345678-9abc.cloud.databricks.com
    http_path: /sql/1.0/warehouses/abc123
    token: ${DATABRICKS_TOKEN}
    catalog: main
    schema: analytics

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PostgreSQL

databases:
  - name: postgres_prod
    type: postgresql
    host: postgres.example.com
    port: 5432
    database: analytics
    user: ${POSTGRES_USER}
    password: ${POSTGRES_PASSWORD}

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Accessors

Accessors define what information to extract from each table. Each accessor generates a specific context file:
  • columns — Column names, data types, and schema information
  • preview — Sample data rows (first N rows of the table)
  • description — Table descriptions and metadata
  • profiling — Data profiling statistics (null counts, unique values, min/max, etc.)
You can select which accessors to use based on your needs: 
databases:
  - name: my_database
    type: bigquery
    # ... connection details ...
    accessors:
      - columns      # Always recommended
      - preview      # Useful for understanding data format
      - description  # Adds business context
      - profiling    # Helpful for data quality insights

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Synchronization

Once configured, sync your database schemas: 
dazense sync
This will:
  1. Connect to your database
  2. Extract schema information based on your accessors
  3. Save it to context/databases/ as text files
  4. Make it available to your agent

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Context Files

After syncing, you’ll see a hierarchical structure like: 
context/
└── databases/
    └── type=bigquery/database=dazense-production/schema=prod_silver/
        ├── table=dim_users/
        │   ├── columns.md         # Column definitions and types
        │   ├── description.md     # Table description
        │   ├── preview.md         # Sample data preview
        │   └── profiling.md       # Data profiling information
        └── table=customers/
            ├── columns.md
            ├── description.md
            ├── preview.md
            └── profiling.md
Each table has files corresponding to the accessors you’ve configured.

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Table Selection

Control which tables to sync using include and exclude patterns. Pattern Syntax Use glob patterns to match table names:
  • schema_name.table_name — Exact match
  • schema_name.* — All tables in a schema
  • *.table_name — Table name across all schemas
  • *_staging — Tables ending with _staging
  • test_* — Tables starting with test_
  • * — All tables
Include Patterns Specify which tables to sync (if empty, all tables are included): 
databases:
  - name: production_warehouse
    type: snowflake
    # ... connection details ...
    include:
      - analytics.*        # All tables in analytics schema
      - marts.dim_*        # All dimension tables in marts
      - marts.fct_*        # All fact tables in marts
Exclude Patterns Specify which tables to exclude from syncing: 
databases:
  - name: production_warehouse
    type: snowflake
    # ... connection details ...
    exclude:
      - *.temp_*           # Exclude all temp tables
      - staging.*          # Exclude entire staging schema
      - *_test             # Exclude tables ending with _test
      - *_backup           # Exclude backup tables
Combined Example You can use both include and exclude together: 
databases:
  - name: production_warehouse
    type: snowflake
    # ... connection details ...
    include:
      - analytics.*        # Start with all analytics tables
      - marts.*            # And all marts tables
    exclude:
      - *.temp_*           # But exclude temp tables
      - *_staging          # And staging tables
      - *_backup           # And backup tables
When both include and exclude are specified, inclusion filtering is applied first, then exclusion rules filter from that result.

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Best Practices

When selecting tables to include in your context, aim for the optimal balance:
  • Include enough tables to answer your users’ questions without exploratory queries
  • Exclude irrelevant tables to reduce token costs and improve focus
  • Start small with core business tables, then expand based on usage