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Orchest vs Dask: What are the differences?
Orchest: An open source tool for creating data science pipelines. 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; Dask: A flexible library for parallel computing in Python. It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers. .
Orchest and Dask can be primarily classified as "Data Science" tools.
Some of the features offered by Orchest are:
- Visual pipeline editor
- Executable notebooks
- Open source
On the other hand, Dask provides the following key features:
- Supports a variety of workloads
- Dynamic task scheduling
- Trivial to set up and run on a laptop in a single process