Anaconda vs Orchest

Overview

Anaconda

Stacks437

Followers490

Votes0

Orchest

Stacks1

Followers12

Votes0

GitHub Stars4.1K

Forks264

Orchest vs Anaconda: What are the differences?

Developers describe Orchest as "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. On the other hand, Anaconda is detailed as "The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders". 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.

Orchest and Anaconda can be categorized as "Data Science" tools.

Detailed Comparison

Anaconda	Orchest
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.	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.
Stay safe and secure; Deliver on your data strategy; Get to market faster; Maximize flexibility and control	Visual pipeline editor; Executable notebooks; Open source
Statistics
GitHub Stars -	GitHub Stars 4.1K
GitHub Forks -	GitHub Forks 264
Stacks 437	Stacks 1
Followers 490	Followers 12
Votes 0	Votes 0
Integrations
Python PyCharm Visual Studio Code Atom-IDE Visual Studio	Pandas dbt Python R Language Matplotlib TensorFlow Streamlit PyTorch Dask Jupyter

What are some alternatives to Anaconda, Orchest?

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

NumPy

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.

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

SciPy

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.

Dataform

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

PySpark

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.

Pentaho Data Integration

It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.

Dask

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.

KNIME

It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.