Wendelin

Wendelin originated from an idea of Jean-Paul Smets and Alexandre Gramfort and was launched by Nexedi at the MariaDB conference in 2014. Thanks to the support of Systematic GTLL, PIA, Wölfel and to contributions from Abilian, INRIA, Engie, Mitsubishi Motors Russia and Paris 13 University, it evolved into a mature, python-native platform for collecting, transforming and visualising streams or batches of data in an industrial context. The Wendelin data hub was jointly developed by Nexedi and Télécom ParisTech.

Wendelin = scikit-learn + NEO

Wendelin combines the performance of scikit-learn machine learning with NEO distributed storage in order to provide out-of-core processing of large data sets. Main application fields are industrial big data collection, processing and storage. Any industrial problem of prediction can be adressed with Wendelin: mechanical health prediction, intrusion prediction, power prediction, et al. In addition, the support of other NumPy based libraries such as OpenCV or Pandas, allows Wendelin to be used in other fields such as video processing or finance.

Installation

Wendelin is easy to set up and get working. There are two ways to setup Wendelin - Wendelin Standalone  and Wendelin on the cloud. To install wendelin on the cloud follow the Wendelin Big Data Learning Track.

Documentation

• Wendelin Architecture is based on 5 layers, and provides key features not found in other platforms.
• Wendelin vs. Hadoop comparison table.
• Wendelin Big Data Learning Track contains a sequence of lectures teaching how to setup and use Wendelin.
• HowTos, FAQs and Tutorials provide all the information you need to get started with Wendelin as a developer. 

Source Code

• Wendelin - Wendelin Gitlab Repository
• wendelin.core (also in pypi) - Out-of-Core processing module
• Fluentd Wendelin plugin
• Fluentd binary Wendelin plugin
• ebulk Wendelin plugin

Tests

Automatic tests for Wendelin are run within the Nexedi test environment. The latest test results can be seen in the Nexedi Test Status for Wendelin.

Examples

Nexedi is working with Wendelin on client implementations and research projects. Please refer to the following examples for ideas on how Wendelin can be used:

•  Wendelin Big Data Set Repository - an application maintained by Nexedi for publicly available big data sets

Support Services

Aside from support, consulting and custom development provided by Nexedi, Wendelin can be extended with open source or proprietary components to fit a given vertical big data market.

The Wendelin project is looking for industrial partners willing to adapt Wendelin to more vertical markets and reinvest part of their revenue into Wendelin core and in particular into scikit-learn.

Licence

Wendelin is Free Software, licensed under the terms of the GNU GPL v3 (or later). For rationale, please see Nexedi licensing.