What is Orchest?
It is a web-based data science tool that works on top of your filesystem allowing you to use your editor of choice. With Orchest you get to focus on visually building and iterating on your pipeline ideas. Under the hood Orchest runs a collection of containers to provide a scalable platform that can run on your laptop as well as on a large scale cloud cluster.
Orchest is a tool in the Data Science Tools category of a tech stack.
Orchest is an open source tool with 634 GitHub stars and 37 GitHub forks. Here’s a link to Orchest's open source repository on GitHub
Python, TensorFlow, R Language, Pandas, and Jupyter are some of the popular tools that integrate with Orchest. Here's a list of all 8 tools that integrate with Orchest.
- Visual pipeline editor
- Executable notebooks
- Open source
Orchest Alternatives & Comparisons
What are some alternatives to Orchest?
See all alternatives
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.