Dask vs. Lucidworks Fusion vs. Python vs. broot Comparison Chart

Fusion transforms your siloed data into personalized insights unique to each user. Lucidworks Fusion lets customers easily deploy AI-powered data discovery and search applications in a modern, containerized, cloud-native architecture. Data scientists interact with those applications by leveraging existing machine learning models and workflows. Or they can quickly create and deploy new models using popular tools like Python ML, TensorFlow, scikit-learn, and spaCy. Reduce the effort and risk of managing deployments of Fusion in the cloud. Lucidworks has modernized Fusion with a cloud-native microservices architecture orchestrated by Kubernetes. Fusion allows customers to dynamically manage application resources as utilization ebbs and flows, reduce the effort of deploying and upgrading Fusion, and avoid unscheduled downtime and performance degradation. Fusion includes native support for Python machine learning models. Plug your custom ML models into Fusion.

The ROOT data analysis framework is used much in High Energy Physics (HEP) and has its own output format (.root). ROOT can be easily interfaced with software written in C++. For software tools in Python there exists pyROOT. Unfortunately, pyROOT does not work well with python3.4. broot is a small library that converts data in python numpy ndarrays to ROOT files containing trees with a branch for each array. The goal of this library is to provide a generic way of writing python numpy datastructures to ROOT files. The library should be portable and supports both python2, python3, ROOT v5 and ROOT v6 (requiring no modifications on the ROOT part, just the default installation). Installation of the library should only require a user to compile to library once or install it as a python package.