It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment. | 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. |
Graphical user interface;
Analysis processes design;
Multiple data management methods;
Data from file, database, web, and cloud services;
In-memory, in-database and in-Hadoop analytics;
Application templates;
-D graphs, scatter matrices, self-organizing map;
GUI or batch processing | Supports a variety of workloads;Dynamic task scheduling ;Trivial to set up and run on a laptop in a single process;Runs resiliently on clusters with 1000s of cores |
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