See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
Get the guidedbt Cloud is a hosted service designed to simplify the process of data transformation, empowering analytics engineering teams to build and manage data transformations. Although it enhances the capabilities provided by dbt Core, particularly for teams that require collaboration, it does not natively support direct connections to Microsoft SQL Server. Instead, dbt Cloud relies on adapters to connect to various databases, and for SQL Server, the dbt-sqlserver adapter is utilized. However, this adapter is not supported by dbt Cloud, which poses a limitation for those seeking to use dbt Cloud with SQL Server. For those interested in exploring other data platforms, dbt Cloud offers several connection options.
The dbt-sqlserver adapter is compatible with dbt Core 0.14.0 and newer versions, providing a bridge between dbt's transformation capabilities and SQL Server's database management features. This setup supports a range of SQL Server versions, including 2016, 2017, 2019, 2022, and the Azure SQL Database.
Installing the dbt-sqlserver adapter is straightforward, but it must be noted that from version 1.8 onwards, the adapter doesn't automatically include dbt-core. This requires a separate installation step. The installation process involves using pip, the package installer for Python, ensuring that Python is installed on your system and up-to-date.
The installation command is as follows:
pip install dbt-sqlserver
This installation is essential for enabling dbt Core to interface with SQL Server databases, given the lack of native support in dbt Cloud.
The dbt-sqlserver adapter supports a variety of configurations, making it versatile for different SQL Server environments. Understanding these configurations is crucial for successful integration.
The adapter offers several methods for authentication, which are pivotal for accessing databases securely:
These methods cater to diverse authentication needs, from simple login credentials to more complex identity services provided by Azure.
Security is a primary concern when connecting to databases. With ODBC Driver 18, connections are encrypted by default, ensuring data in transit is protected. This encryption feature is enabled with the encrypt parameter set to true. Additionally, server certificates are validated by default (trust_cert is false), further bolstering security by verifying the server's identity.
To configure dbt for SQL Server, you'll need to edit the profiles.yml file. Below is an example configuration that outlines the fundamental parameters:
your_profile_name:
target: dev
outputs:
dev:
type: sqlserver
driver: 'ODBC Driver 18 for SQL Server'
server: hostname or IP of your server
port: 1433
database: exampledb
schema: schema_name
authentication: ActiveDirectoryPassword
user: bill.gates@microsoft.com
password: iheartopensource
These parameters should be adjusted according to your specific environment and security requirements.
Before establishing a connection between dbt and SQL Server, certain prerequisites must be met to ensure successful connectivity. The essential requirements include the installation of the ODBC driver, especially version 18, and the installation of ODBC header files on Linux systems like Debian/Ubuntu.
For Linux systems, you can install the ODBC header files using:
sudo apt install unixodbc-dev
Meeting these prerequisites ensures that your system can support the necessary drivers and dependencies for successful connectivity.
The flexibility of the dbt-sqlserver adapter makes it suitable for both on-premise SQL Server deployments and Azure SQL environments. This versatility enables organizations to leverage the power of dbt's data transformation capabilities across diverse SQL Server infrastructures. For users interested in connecting dbt Cloud to Microsoft Fabric, similar configurations can be applied.
Feature On-Premise SQL Server Azure SQL Database Deployment Local server infrastructure Cloud-based platform by Microsoft Scalability Limited by hardware Highly scalable and flexible Maintenance Requires internal IT support Managed by Microsoft Cost Structure Capital expenditure Pay-as-you-go model Authentication Methods SQL Server, Windows Credentials Azure AD, Managed Identity
This table highlights the differences in deployment, scalability, and maintenance between on-premise SQL Server and Azure SQL Database, illustrating how the dbt-sqlserver adapter can adapt to each environment's unique characteristics.
Despite the capabilities of the dbt-sqlserver adapter, several challenges exist when integrating dbt Cloud with SQL Server. The lack of direct support in dbt Cloud necessitates reliance on dbt Core, which can complicate the setup process. Additionally, the detailed configuration required in profiles.yml and dependency management can be cumbersome, particularly for teams unfamiliar with dbt or SQL Server configurations.
Security considerations are also paramount, especially in cloud environments, where ensuring secure connections demands careful attention to encryption and authentication methods. These challenges necessitate a thorough understanding of both dbt and SQL Server to ensure successful integration and operation.
Secoda is a data management platform that leverages AI to centralize and streamline data discovery, lineage tracking, governance, and monitoring across an organization's entire data stack. By acting as a "second brain" for data teams, it provides a single source of truth, enabling users to easily find, understand, and trust their data. This is achieved through features like search, data dictionaries, and lineage visualization, ultimately improving data collaboration and efficiency within teams.
Secoda's comprehensive approach to data management ensures that both technical and non-technical users can access and understand the data they need quickly and efficiently.
Secoda enhances data discovery by allowing users to search for specific data assets across their entire data ecosystem using natural language queries. This feature makes it easy for users to find relevant information regardless of their technical expertise. Additionally, its data lineage tracking automatically maps the flow of data from its source to its final destination, providing complete visibility into how data is transformed and used across different systems.
Secoda leverages machine learning to extract metadata, identify patterns, and provide contextual information about data. This enhances the understanding of data by offering AI-powered insights that can significantly improve data accessibility and analysis speed. By quickly identifying data sources and lineage, users can spend less time searching for data and more time analyzing it.
Secoda enables streamlined data governance by centralizing processes, making it easier to manage data access and compliance. It provides granular access control and data quality checks to ensure data security and compliance. Furthermore, Secoda's collaboration features allow teams to share data information, document data assets, and collaborate on data governance practices, ultimately enhancing data quality and collaboration within teams.
By monitoring data lineage and identifying potential issues, teams can proactively address data quality concerns, ensuring that data remains accurate and reliable.
Try Secoda today and experience a significant boost in data accessibility, analysis speed, and governance efficiency. Our platform offers a seamless setup and long-term benefits that will improve your data management processes.
Take the first step towards transforming your data management by getting started today.
Built for data teams, designed for everyone so you can get more from your data stack.
© 2025 Secoda, Inc. All rights reserved.
By using this website, you accept our Terms of Use and Privacy Policy.
