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 guideDuckDB is an embedded database uniquely designed for OLAP-style analytics, excelling in analytical query processing. It is lightweight and can be embedded within applications, offering high-performance SQL analytics without the overhead of traditional database systems. dbt (data build tool) is widely used in data transformation workflows, offering SQL-based transformations and is known for its version control and testing capabilities. Integrating DuckDB with dbt allows users to leverage powerful data transformation functions in a lightweight, embedded analytics environment.
To connect DuckDB with dbt, you utilize the dbt-duckdb adapter. This integration is straightforward but requires specific steps for installation and configuration, ensuring compatibility between the two systems.
The first step in connecting DuckDB with dbt is installing the dbt-duckdb adapter. This adapter is crucial as it enables dbt to communicate with DuckDB, allowing transformations to be executed directly within the DuckDB environment.
To install the dbt-duckdb adapter, use the Python package manager pip. It's essential to note that starting from version 1.8, installing dbt-duckdb does not automatically include dbt-core. Thus, users must ensure dbt-core is also installed if it’s not already. Compatibility is key, so ensure you are using dbt Core version v1.0.1 or newer, and DuckDB should be version 0.3.2 or higher to use the adapter effectively.
Once the dbt-duckdb adapter is installed, configuration is the next step. The configuration involves setting up the profiles.yml file to define how dbt connects to DuckDB.
Set the type to duckdb to specify the adapter type. Define the path pointing to the local filesystem path for the DuckDB database file and log. Optional configuration settings include specifying a non-default schema, loading additional DuckDB extensions, and custom configurations like S3 connections or handling parquet files with AWS credentials.
This configuration is crucial for enabling dbt to perform data transformations within DuckDB, utilizing the specified environment and settings.
Integrating DuckDB with dbt offers several advantages and challenges that need to be considered:
The dbt-duckdb adapter is maintained by community authors, including contributors like Josh Wills. This community-driven approach ensures continuous improvements and updates, although it lacks the formal support structure of official dbt integrations.
dbt Core supports versions v1.0.1 and newer, while DuckDB requires version 0.3.2 or higher to ensure all functionalities are accessible. Contributions come from various developers and data enthusiasts, ensuring the adapter stays updated with the latest features and fixes.
The integration of DuckDB with dbt facilitates efficient data workflows by combining the strengths of both tools. dbt provides robust transformation capabilities, allowing users to write SQL-based transformations that can be executed within DuckDB's high-performance engine. With support for extensions, users can scale their analytics capabilities, including handling complex data formats like parquet files directly within DuckDB. Although not supported by dbt Cloud, the local environment setup provides flexibility for developers to test and deploy analytics workflows efficiently.
Secoda revolutionizes data discovery by allowing users to search for specific data assets using natural language queries. This feature makes it easy for individuals, regardless of their technical expertise, to find the relevant information they need across their entire data ecosystem. By centralizing the data discovery process, Secoda ensures that users can quickly locate and access data, leading to improved data collaboration and efficiency within teams.
In addition to simplifying the search process, Secoda provides a single source of truth through features like data dictionaries and lineage visualization. These tools help users understand and trust their data by offering context and clarity, making it easier to collaborate and make informed decisions.
AI is at the core of Secoda's data management platform, enhancing its ability to extract metadata, identify patterns, and provide contextual information about data. By leveraging machine learning, Secoda offers AI-powered insights that improve data understanding, making it easier for users to gain valuable insights from their data.
Secoda's AI capabilities extend to data lineage tracking, where it automatically maps the flow of data from its source to its final destination. This provides complete visibility into how data is transformed and used across different systems, ensuring that users have a clear understanding of their data's journey.
Secoda streamlines data governance by centralizing processes, making it easier to manage data access and compliance. The platform enables granular access control and data quality checks, ensuring data security and adherence to regulations. By offering a unified solution for data governance, Secoda simplifies the management of data assets and enhances data quality.
Additionally, Secoda's collaboration features allow teams to share data information, document data assets, and collaborate on data governance practices. This fosters a culture of transparency and accountability, empowering teams to proactively address data quality concerns and improve overall data management.
Try Secoda today and experience a significant boost in data collaboration and efficiency. Our platform acts as a "second brain" for data teams, providing quick and easy access to information about your data.
Don't wait any longer, get started today with Secoda and transform your data management practices.
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.
