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  1. Stackups
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  4. Data Science Tools
  5. Facette vs Orchest

Facette vs Orchest

OverviewComparisonAlternatives

Overview

Facette
Facette
Stacks0
Followers4
Votes0
GitHub Stars1.2K
Forks68
Orchest
Orchest
Stacks1
Followers12
Votes0
GitHub Stars4.1K
Forks264

Orchest vs Facette: 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, Facette is detailed as "Time series data visualization software". It is a web application to display time series data from various sources such as collectd, Graphite, InfluxDB or KairosDB on graphs, designed to be easy to setup and to use.

Orchest and Facette belong to "Data Science Tools" category of the tech stack.

Facette is an open source tool with 1.11K GitHub stars and 78 GitHub forks. Here's a link to Facette's open source repository on GitHub.

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Detailed Comparison

Facette
Facette
Orchest
Orchest

It is a web application to display time series data from various sources such as collectd, Graphite, InfluxDB or KairosDB on graphs, designed to be easy to setup and to use.

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.

Data Visualization, Time series data
Visual pipeline editor; Executable notebooks; Open source
Statistics
GitHub Stars
1.2K
GitHub Stars
4.1K
GitHub Forks
68
GitHub Forks
264
Stacks
0
Stacks
1
Followers
4
Followers
12
Votes
0
Votes
0
Integrations
No integrations available
Pandas
Pandas
dbt
dbt
Python
Python
R Language
R Language
Matplotlib
Matplotlib
TensorFlow
TensorFlow
Streamlit
Streamlit
PyTorch
PyTorch
Dask
Dask
Jupyter
Jupyter

What are some alternatives to Facette, Orchest?

Pandas

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

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

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

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

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

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.

Anaconda

Anaconda

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.

Dask

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.

Pentaho Data Integration

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.

StreamSets

StreamSets

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

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