Streamline Python Projects with uv

In data engineering, innovation in tooling is not just an advantage – it’s a necessity. Organizations handle growing datasets and increasingly complex workflows, which creates a need for tools that streamline processes and enhance performance of data pipelines further. At Deloitte, we’re committed to stay ahead in our field. Our Cloud and Data Engineering community in Finland is always looking for ways to improve workflow efficiency and enhance reliability of the platforms we work on. One of the tools that has drawn our attention is uv, a next-generation Python package manager.

What is uv?

uv is a modern Python package manager that aims to revolutionize the way developers manage environments and dependencies. It tries to address many of the challenges associated with traditional package management tools with speed and efficiency. These capabilities are important for data engineers and platform architects maintaining robust workflows. Dependency and environment management are critical components of the software development lifecycle. Poorly managed dependencies can lead to compatibility issues, and inefficiencies in project development.

Key features of uv

• Fast Dependency Resolution: Guarantees reproducibility, allowing consistent dependency installations across all environments with universal lock file.
• Improved Performance: 10-115x faster than existing tools, leveraging Rust's performance capabilities, dependency caching.
• Consolidated Workflow: Replaces multiple tools, offering a single command-line interface for everything from dependency resolution to project publishing.
• Seamless Environment Management: Simplifies creating and managing virtual environments, ensuring projects remain isolated and free from dependency conflicts.

By introducing these features, uv provides a robust foundation for managing Python packages. This gives data engineers potential to maintain cleaner and more efficient workflows.

Testing and comparing uv against existing tools

While uv introduces several advancements, it retains core functionalities familiar to users from existing tools such as pip and poetry. For example,

'uv add' and 'uv remove' correspond closely to pip’s 'pip install' and 'pip uninstall'. These commands handle dependency management by automatically updating the pyproject.toml file, regenerating the lock file, and syncing the virtual environment. uv’s lock file ensures reproducibility across systems. Overall, the commands used in uv are close to commands of existing tools.  

Compared to Poetry, uv extends reproducibility by syncing dependencies automatically. For example, adding a package using 'uv add' updates the project file and lock file while syncing the environment, offering an all-in-one approach not seen in pip or poetry. Another notable feature is uv’s support for dependency groups. This is useful for scenarios requiring separate dev, production, or testing dependencies. For example, you can add production-only dependencies with 'uv add --group production'. The dependencies are automatically then organized within the pyproject.toml file. Installing specific groups is also straightforward with 'uv sync --group production' in our example case.

As the comparisons shows, it is straightforward to use uv as one practical tool to replace currently used multiple tools. But as many existing articles suggest, the main benefit of uv is its performance. Dependency resolution and installation with uv is said to be significantly faster which is an advantage particularly in iterative development cycles or CI/CD pipelines. We tested uv's performance by resolving and installing dependencies of a few trending open-source repositories from GitHub.

The performance of uv was indeed better compared to Poetry, as many articles suggested. uv was able to resolve and install the dependencies without problems by reading pyproject.toml files from the Python projects.

With all this considered, uv has the potential to replace existing tools like pip and poetry for many use cases. Its features make the tool worth trying out for developers and data engineers aiming to optimize their environments and dependencies.

If you’re looking to explore ways to enhance your dependency management and streamline your development workflow, you can try uv yourself by setting up a test project with uv init - and see if uv can support you in your workflow management.

About Us

At Deloitte Finland, our Cloud & Data Engineering practice helps organizations build scalable, secure, and future-ready data platforms. We work across industries to lay strong data foundations, streamline analytics, and enable GenAI use cases.

We support clients throughout their data & technology journey – from early planning and architecture to implementation, scaling, and operations. This includes building platform solutions, defining ways of working, and delivering real use cases that bring data to life. Whether it’s targeted support, a challenging use case, or large-scale delivery, we adapt to what each situation calls for.

We work with a wide range of clients and technology stacks across the Nordics – from large enterprises to fast-moving teams , and are always happy to collaborate on how we can turn your data challenges into practical solutions