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Version: 0.3.0

Configuring Data Warehouses

Version: 0.3.0

DecisionBox connects to your existing data warehouse in read-only mode. This guide covers setup for each supported warehouse.

Google BigQuery​

Prerequisites​

  • A GCP project with BigQuery datasets
  • One of:
    • On GCP: Application Default Credentials (Workload Identity, gcloud auth)
    • Outside GCP: A service account JSON key

Dashboard Setup​

  1. Select Google BigQuery as warehouse provider
  2. Fill in:
    • Project ID: Your GCP project ID (e.g., my-gcp-project)
    • Location: Dataset location (e.g., US, us-central1, us-east5)
  3. Enter Datasets: Comma-separated (e.g., analytics, features_prod)
  4. Optionally set Filter: field + value for multi-tenant data

Authentication​

When creating a project, select one of the available auth methods:

Application Default Credentials (ADC) — No credentials needed. Works automatically on GKE (Workload Identity), Cloud Run, Compute Engine, or after gcloud auth application-default login.

Service Account Key — For cross-cloud, local, or federated access.

  1. Create a service account in GCP Console with BigQuery Data Viewer and BigQuery Job User roles
  2. Download the JSON key
  3. In the project creation form, select Service Account Key as auth method and paste the JSON key

This method also supports Workload Identity Federation (WIF) — keyless access from AWS, Azure, or any OIDC identity provider. Instead of a service account key, paste the WIF credential config JSON generated by gcloud iam workload-identity-pools create-cred-config. The GCP SDK auto-detects whether the JSON is a service account key or a WIF credential config.

Multi-Dataset Support​

BigQuery projects can have multiple datasets. List all datasets you want the agent to explore:

Datasets: events_prod, features_prod, analytics

The agent discovers table schemas from all listed datasets and can query across them.

Filtering​

For shared datasets with data from multiple apps/tenants:

Filter Field: app_id
Filter Value: 68a42f378e3b227c8e41b0e5

The agent adds WHERE app_id = '68a42f378e3b227c8e41b0e5' to all queries.

Cost​

BigQuery charges per TB scanned (default: $7.50/TB for on-demand pricing). The cost estimation feature uses BigQuery's dry-run API to preview costs before running.

Amazon Redshift​

Prerequisites​

  • A Redshift cluster (provisioned) or Redshift Serverless workgroup
  • AWS credentials with Redshift Data API access

Dashboard Setup — Serverless​

  1. Select Amazon Redshift as warehouse provider
  2. Fill in:
    • Workgroup Name: Your Serverless workgroup (e.g., default-workgroup)
    • Database: Database name (e.g., dev)
    • Region: AWS region (e.g., us-east-1)
  3. Enter Datasets: Schema names (e.g., public)

Dashboard Setup — Provisioned​

  1. Select Amazon Redshift as warehouse provider
  2. Fill in:
    • Cluster Identifier: Your cluster ID (e.g., my-redshift-cluster)
    • Database: Database name
    • Region: AWS region
  3. Enter Datasets: Schema names

Authentication​

When creating a project, select one of the available auth methods:

IAM Role — No credentials needed. Works automatically on EKS (pod IAM role), EC2 (instance profile), or with environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY).

Access Keys — For cross-cloud or local access. Enter your AWS access key pair in the format ACCESS_KEY_ID:SECRET_ACCESS_KEY. The IAM user needs these permissions:

  • redshift-data:ExecuteStatement, redshift-data:DescribeStatement, redshift-data:GetStatementResult
  • redshift-serverless:GetCredentials (Serverless) or redshift:GetClusterCredentials (Provisioned)

Assume Role — For cross-account access. Enter the Role ARN of the target role (e.g., arn:aws:iam::123456789012:role/RedshiftRole). Optionally provide an External ID if the role's trust policy requires one. The agent assumes this role via STS using its base credentials (IAM role or environment).

How Redshift Queries Work​

DecisionBox uses the Redshift Data API (not JDBC), which works asynchronously:

  1. ExecuteStatement — Submit SQL
  2. DescribeStatement — Poll until complete
  3. GetStatementResult — Fetch results

This means no JDBC driver is needed, and it works with both Serverless and Provisioned clusters.

Data Type Handling​

Redshift types are automatically normalized:

  • INTEGER, BIGINT, SMALLINT → INT64
  • VARCHAR, TEXT, CHAR → STRING
  • DECIMAL, NUMERIC → FLOAT64 (parsed from column metadata, not string guessing)
  • BOOLEAN → BOOL
  • TIMESTAMP, TIMESTAMPTZ → TIMESTAMP

System Table Filtering​

The agent automatically excludes Redshift system tables from discovery:

  • pg_* tables
  • stl_* tables (system log)
  • svv_* tables (system views)

Snowflake​

Prerequisites​

  • A Snowflake account (trial or production)
  • Username with access to the target database and schema
  • A virtual warehouse (e.g., COMPUTE_WH)

Dashboard Setup​

  1. Select Snowflake as warehouse provider
  2. Fill in:
    • Account Identifier: Your Snowflake account (e.g., ORGNAME-ACCOUNTNAME)
    • Username: Snowflake user
    • Warehouse: Virtual warehouse name (e.g., COMPUTE_WH)
    • Database: Database name (e.g., ANALYTICS_DB)
  3. Optionally set Schema (default: PUBLIC) and Role
  4. Enter Datasets: Schema names (e.g., PUBLIC)

Authentication​

When creating a project, select one of the available auth methods:

Username / Password — Enter your Snowflake password.

Key Pair (JWT) — Recommended for production.

  1. Generate an RSA key pair: 
    openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt
    openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub
  2. Assign the public key to your Snowflake user: 
    ALTER USER my_user SET RSA_PUBLIC_KEY='MIIBIjANBg...';
  3. In the project creation form, select Key Pair (JWT) and paste the PEM private key content.

Data Type Handling​

Snowflake types are automatically normalized:

  • NUMBER, INT, BIGINT, SMALLINT, TINYINT, BYTEINT → INT64 (in schema metadata)
  • FLOAT, DOUBLE, REAL, DECIMAL(p,s), NUMERIC(p,s) → FLOAT64
  • In query results, NUMBER values with decimals are returned as FLOAT64 (the driver reports actual precision)
  • VARCHAR, STRING, CHAR, TEXT → STRING
  • BOOLEAN → BOOL
  • DATE → DATE
  • TIMESTAMP_NTZ, TIMESTAMP_LTZ, TIMESTAMP_TZ → TIMESTAMP
  • VARIANT, OBJECT, ARRAY → RECORD (JSON string in query results)
  • BINARY, VARBINARY → BYTES

Schema Metadata​

The provider uses INFORMATION_SCHEMA for table listing and column metadata. Row counts come from INFORMATION_SCHEMA.TABLES.ROW_COUNT — no full-table scans needed.

Cost​

Snowflake charges per-second based on warehouse size (credits per hour). There is no dry-run API, so the cost estimation feature is not available for Snowflake.

PostgreSQL​

Prerequisites​

  • A PostgreSQL 12+ server accessible from the DecisionBox deployment
  • A database user with read access to the target schema
  • SSL configured (recommended for remote connections)

Dashboard Setup​

  1. Select PostgreSQL as warehouse provider
  2. Fill in:
    • Host: Database hostname (e.g., db.example.com)
    • Port: Database port (default: 5432)
    • Database: Database name
    • Username: Database user
  3. Optionally set Schema (default: public) and SSL Mode (default: require)
  4. Enter Datasets: Schema names (e.g., public)

Authentication​

When creating a project, select one of the available auth methods:

Username / Password — Enter host, port, database, username, and password. The connection uses the lib/pq driver with the sslmode you configure.

Connection String — For advanced configurations (Heroku, RDS, Cloud SQL, Supabase). Enter a full PostgreSQL connection string:

postgres://user:password@host:5432/dbname?sslmode=require

This method supports all lib/pq DSN parameters including sslmode, connect_timeout, search_path, and application_name.

SSL Mode​

ModeDescription
disableNo SSL (only for localhost or trusted networks)
allowTry non-SSL first, fall back to SSL
preferTry SSL first, fall back to non-SSL (common RDS default)
requireSSL required, no certificate verification (default)
verify-caSSL with CA certificate verification
verify-fullSSL with CA + hostname verification (most secure)

Data Type Handling​

PostgreSQL types are automatically normalized:

  • INTEGER, BIGINT, SMALLINT, SERIAL, BIGSERIAL → INT64
  • REAL, DOUBLE PRECISION → FLOAT64
  • NUMERIC, DECIMAL → FLOAT64 (parsed from driver's []byte representation)
  • VARCHAR, TEXT, CHAR, UUID, INET, CIDR, INTERVAL, MONEY → STRING
  • BOOLEAN → BOOL
  • DATE → DATE
  • TIMESTAMP, TIMESTAMPTZ → TIMESTAMP
  • JSON, JSONB → RECORD (JSON string in query results)
  • BYTEA → BYTES
  • ARRAY types → STRING (PostgreSQL text representation, e.g., {1,2,3})

Schema Metadata​

The provider uses information_schema.tables and information_schema.columns for table listing and column metadata. Row counts come from pg_class.reltuples — an estimate maintained by PostgreSQL's autovacuum, no full-table scans needed.

Cost​

PostgreSQL is self-hosted or managed (RDS, Cloud SQL, AlloyDB, Supabase). There is no per-query cost model, so the cost estimation feature is not available.

Databricks​

Prerequisites​

  • A Databricks workspace (AWS, Azure, or GCP)
  • A SQL warehouse (serverless or classic)
  • Unity Catalog enabled with access to the target catalog and schema

Dashboard Setup​

  1. Select Databricks as warehouse provider
  2. Fill in:
    • Server Hostname: Your workspace hostname (e.g., xxx.cloud.databricks.com)
    • HTTP Path: SQL warehouse endpoint (e.g., /sql/1.0/warehouses/xxx)
    • Catalog: Unity Catalog catalog name (e.g., main)
  3. Optionally set Schema (default: default)
  4. Enter Datasets: Schema names (e.g., default)

Authentication​

When creating a project, select one of the available auth methods:

Personal Access Token (PAT) — Simplest setup.

  1. In Databricks, go to Settings > Developer > Access tokens
  2. Generate a new token
  3. In the project creation form, select Personal Access Token and paste the token

OAuth M2M (Service Principal) — Recommended for production.

  1. Create a service principal in your Databricks workspace
  2. Create an OAuth secret for the service principal
  3. Grant the service principal access to your SQL warehouse and catalog
  4. In the project creation form, select OAuth M2M and enter client_id:client_secret

Unity Catalog Namespace​

Databricks uses a 3-level namespace: catalog.schema.table. The agent qualifies table references as catalog.information_schema.tables for metadata queries. SQL queries use the schema set in the project's datasets configuration.

Data Type Handling​

Databricks types are automatically normalized:

  • TINYINT, SMALLINT, INT, BIGINT → INT64
  • FLOAT, DOUBLE → FLOAT64
  • DECIMAL(p,s) → FLOAT64 (parsed from driver's string representation)
  • STRING, CHAR, VARCHAR → STRING
  • BOOLEAN → BOOL
  • DATE → DATE
  • TIMESTAMP, TIMESTAMP_NTZ → TIMESTAMP
  • BINARY → BYTES
  • STRUCT, ARRAY, MAP, VARIANT → RECORD (JSON string in query results)
  • INTERVAL → STRING

SQL Dialect​

Databricks SQL extends ANSI SQL with:

  • QUALIFY — Filter window function results directly (like Snowflake)
  • PIVOT / UNPIVOT — Rotate rows to columns and vice versa
  • explode / explode_outer — Expand arrays and maps into rows
  • Delta time travel — TIMESTAMP AS OF and VERSION AS OF
  • Java SimpleDateFormat — Use yyyy-MM-dd (not YYYY-MM-DD)

Cost​

Databricks SQL warehouses charge per DBU (Databricks Unit) based on cluster size and runtime. There is no per-query cost estimation API, so the cost estimation feature is not available.

Cross-Cloud Authentication​

DecisionBox supports accessing warehouses from a different cloud:

ScenarioAuth MethodHow
BigQuery from AWS/AzureService Account KeyPaste SA key JSON or WIF credential config
BigQuery via WIF (keyless)Service Account KeyPaste WIF credential config from gcloud iam workload-identity-pools create-cred-config
Redshift from GCP/AzureAccess KeysPaste ACCESS_KEY_ID:SECRET_ACCESS_KEY
Redshift cross-accountAssume RoleEnter Role ARN + External ID
Snowflake from any cloudPassword or Key PairPaste password or PEM private key
PostgreSQL from any cloudPassword or Connection StringEnter credentials or full DSN
Databricks from any cloudPAT or OAuth M2MPaste token or client_id:client_secret
Any from local devADC / IAM RoleConfigure cloud CLI (gcloud auth, aws configure)

The key concept: warehouse credentials are stored encrypted via the secret provider. When creating a project, select the appropriate auth method and enter credentials inline. The agent reads credentials from the secret provider before initializing the warehouse provider.

Next Steps​