dbt-sqlserver

dbt adapter for sql server.

Passing all tests in dbt-integration-tests.

Only supports dbt 0.14 and newer!

• For dbt 0.18.x use dbt-sqlserver 0.18.x
• dbt 0.17.x is unsupported
• dbt 0.16.x is unsupported
• For dbt 0.15.x use dbt-sqlserver 0.15.x
• For dbt 0.14.x use dbt-sqlserver 0.14.x

Easiest install is to use pip:

pip install dbt-sqlserver

On Ubuntu make sure you have the ODBC header files before installing

sudo apt install unixodbc-dev

Authentication

The following is needed for every target definition for both SQL Server and Azure SQL. The sections below details how to connect to SQL Server and Azure SQL specifically.

type: sqlserver
driver: 'ODBC Driver 17 for SQL Server' (The ODBC Driver installed on your system)
server: server-host-name or ip
port: 1433
schema: schemaname

Security

Encryption is not enabled by default, unless you specify it.

To enable encryption, add the following to your target definition. This is the default encryption strategy recommended by MSFT. For more information see this docs page

encrypt: true # adds "Encrypt=Yes" to connection string
trust_cert: false

For a fully-secure, encrypted connection, you must enable trust_cert: false because "TrustServerCertificate=Yes" is default for dbt-sqlserver in order to not break already defined targets.

standard SQL Server authentication

SQL Server credentials are supported for on-prem as well as cloud, and it is the default authentication method for dbt-sqlsever

user: username
password: password

Windows Authentication (SQL Server-specific)

windows_login: True

alternatively

trusted_connection: True

Azure SQL-specific auth

The following pyodbc-supported ActiveDirectory methods are available to authenticate to Azure SQL:

• Auto
• Azure CLI
• Environment-based authentication
• ActiveDirectory Password
• ActiveDirectory Interactive
• ActiveDirectory Integrated
• Service Principal (a.k.a. AAD Application)
• Managed Identity

Usually the automatic option is the easiest one to use since it will work with any configuration already present in your environment, as explained below.

Auto

This will try to authenticate by using the following methods one by one until it finds a valid way to authenticate:

1. Read credentials from environment variables (see environment-based authentication below)
2. Use the managed identity of the system (see MSI below)
3. VS Code: use the account used to log in to the VS Code Azure extension if installed
4. Use the logged account in the Azure CLI if installed (see below)
5. Azure PowerShell: use the account used with Connect-AzAccount in the Azure PowerShell module if installed

To use automatic authentication, set authentication in profiles.yml to Auto:

authentication: Auto

This is the recommended way for authenticating to databases on Azure because it avoids storing credentials in your profile and can resort to different authentication mechanisms depending on the system you're running dbt on.

Azure CLI

Use the authentication of the Azure command line interface (CLI). First, install the Azure CLI, then, log in:

az login

Then, set authentication in profiles.yml to CLI:

authentication: CLI

Environment-based authentication

You can let dbt dynamically use credentials from your environment variables by configuring your profile with environment-based authentication:

authenticaton: environment

You can configure the following environment variables:

Service principal authentication:

• AZURE_CLIENT_ID
• AZURE_TENANT_ID
• AZURE_CLIENT_SECRET or AZURE_CLIENT_CERTIFICATE_PATH

User authenticaton:

• AZURE_USERNAME
• AZURE_PASSWORD
• AZURE_CLIENT_ID

ActiveDirectory Password

Definitely not ideal, but available

authentication: ActiveDirectoryPassword
user: [email protected]
password: i<3opensource?

ActiveDirectory Interactive (Windows only)

Brings up the Azure AD prompt so you can MFA if need be. The downside to this approach is that you must log in each time you run a dbt command!

authentication: ActiveDirectoryInteractive
user: [email protected]

ActiveDirectory Integrated (Windows only)

Uses your machine's credentials (might be disabled by your AAD admins), also requires that you have Active Directory Federation Services (ADFS) installed and running, which is only the case if you have an on-prem Active Directory linked to your Azure AD...

authentication: ActiveDirectoryIntegrated

Service Principal

client_* and app_* can be used interchangeably. Again, it is not recommended to store a service principal secret in plain text in your dbt_profile.yml. The auto, environment or CLI auth methods are preferred over this one.

authentication: ServicePrincipal
tenant_id: tenatid
client_id: clientid
client_secret: clientsecret

Managed Identity

If the system you're running dbt on has a managed identity, then you can configure the authentication like so:

authentication: MSI

Supported features

Materializations

• Table:
  • Will be materialized as columns store index by default (requires SQL Server 2017 as least). (For Azure SQL requires Service Tier greater than S2) To override: {{ config( as_columnstore = false, ) }}
• View
• Incremental
• Ephemeral

Seeds

By default, dbt-sqlserver will attempt to insert seed files in batches of 400 rows. If this exceeds SQL Server's 2100 parameter limit, the adapter will automatically limit to the highest safe value possible.

To set a different default seed value, you can set the variable max_batch_size in your project configuration.

vars:
  max_batch_size: 200 # Any integer less than  or equal to 2100 will do.

Hooks

Custom schemas

Sources

Testing & documentation

• Schema test supported
• Data tests supported from dbt 0.14.1
• Docs

Snapshots

• Timestamp
• Check

But, columns in source table can not have any constraints. If for example any column has a NOT NULL constraint, an error will be thrown.

DBT Utils

Many DBT utils macros are supported, but they require the addition of the tsql_utils dbt package.

You can find the package and installation instructions in the tsql-utils repo.

Indexes

There is now possible to define a regular sql server index on a table. This is best used when the default clustered columnstore index materialisation is not suitable. One reason would be that you need a large table that usually is queried one row at a time.

Clusterad and non-clustered index are supported:

• create_clustered_index(columns, unique=False)
• create_nonclustered_index(columns, includes=False)
• drop_all_indexes_on_table(): Drops current indexex on a table. Only meaningfull if model is incremental.

Example of applying Unique clustered index on two columns, Ordinary index on one column, Ordinary index on one column with another column included

{{
    config({
        "as_columnstore": false, 
        "materialized": 'table',
        "post-hook": [
            "{{ create_clustered_index(columns = ['row_id', 'row_id_complement'], unique=True) }}",
            "{{ create_nonclustered_index(columns = ['modified_date']) }}",
            "{{ create_nonclustered_index(columns = ['row_id'], includes = ['modified_date']) }}",
        ]
    })
}}

Changelog

See the changelog