Databases

Supported Databases

dazense supports all major data warehouses and databases:

Snowflake
BigQuery
Databricks
PostgreSQL
Redshift
MySQL
SQL Server
And more…

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

Database Setup

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

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.

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

PostgreSQL

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

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

Synchronization

Once configured, sync your database schemas:

dazense sync

This will:

Connect to your database
Extract schema information based on your accessors
Save it to context/databases/ as text files
Make it available to your agent

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.

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.

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

Get started

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Context builder

Semantic layer

Trusted Analytics

Context engineering

Cloud & developers

Self-hosting

Supported Databases

Adding a Database

Database Setup

Snowflake

BigQuery

Databricks

PostgreSQL

Accessors

Synchronization

Context Files

Table Selection

Best Practices

​Supported Databases

​Adding a Database

​Database Setup

​Snowflake

​BigQuery

​Databricks

​PostgreSQL

​Accessors

​Synchronization

​Context Files

​Table Selection

​Best Practices

Supported Databases

Adding a Database

Database Setup

Snowflake

BigQuery

Databricks

PostgreSQL

Accessors

Synchronization

Context Files

Table Selection

Best Practices