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Microsoft SQL Server

Install dlt with MS SQLโ€‹

To install the dlt library with MS SQL dependencies, use:

pip install dlt[mssql]

Setup guideโ€‹


The Microsoft ODBC Driver for SQL Server must be installed to use this destination. This cannot be included with dlt's python dependencies, so you must install it separately on your system. You can find the official installation instructions here.

Supported driver versions:

  • ODBC Driver 18 for SQL Server
  • ODBC Driver 17 for SQL Server

You can also configure the driver name explicitly.

Create a pipelineโ€‹

1. Initialize a project with a pipeline that loads to MS SQL by running:

dlt init chess mssql

2. Install the necessary dependencies for MS SQL by running:

pip install -r requirements.txt

or run:

pip install dlt[mssql]

This will install dlt with the mssql extra, which contains all the dependencies required by the SQL server client.

3. Enter your credentials into .dlt/secrets.toml.

For example, replace with your database connection info:

database = "dlt_data"
username = "loader"
password = "<password>"
host = ""
port = 1433
connect_timeout = 15
# trust self signed SSL certificates
# require SSL connection
# send large string as VARCHAR, not legacy TEXT

You can also pass a SQLAlchemy-like database connection:

# keep it at the top of your toml file! before any section starts

You can place any ODBC-specific settings into the query string or destination.mssql.credentials.query TOML table as in the example above.

To connect to an mssql server using Windows authentication, include trusted_connection=yes in the connection string. This method is useful when SQL logins aren't available, and you use Windows credentials.


The username and password must be filled out with the appropriate login credentials or left untouched. Leaving these empty is not recommended.

To connect to a local sql server instance running without SSL pass encrypt=no parameter:


To allow self signed SSL certificate when you are getting certificate verify failed:unable to get local issuer certificate:


*To use long strings (>8k) and avoid collation errors:


To pass credentials directly, use the explicit instance of the destination

pipeline = dlt.pipeline(

Write dispositionโ€‹

All write dispositions are supported.

If you set the replace strategy to staging-optimized, the destination tables will be dropped and recreated with an ALTER SCHEMA ... TRANSFER. The operation is atomic: mssql supports DDL transactions.

Data loadingโ€‹

Data is loaded via INSERT statements by default. MSSQL has a limit of 1000 rows per INSERT, and this is what we use.

Supported file formatsโ€‹

Supported column hintsโ€‹

mssql will create unique indexes for all columns with unique hints. This behavior may be disabled.

Syncing of dlt stateโ€‹

This destination fully supports dlt state sync.

Data typesโ€‹

MS SQL does not support JSON columns, so JSON objects are stored as strings in nvarchar columns.

Additional destination optionsโ€‹

The mssql destination does not create UNIQUE indexes by default on columns with the unique hint (i.e., _dlt_id). To enable this behavior:


You can explicitly set the ODBC driver name:

driver="ODBC Driver 18 for SQL Server"

When using a SQLAlchemy connection string, replace spaces with +:

# keep it at the top of your toml file! before any section starts

dbt supportโ€‹

No dbt support yet.

Additional Setup guidesโ€‹

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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