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NumPy vs Denodo: What are the differences?
Developers describe NumPy as "Fundamental package for scientific computing with Python". 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. On the other hand, Denodo is detailed as "Data virtualisation platform, allowing you to connect disparate data from any source". It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.
NumPy and Denodo can be categorized as "Data Science" tools.
Some of the features offered by NumPy are:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
On the other hand, Denodo provides the following key features:
- Data virtualization
- Data query
- Data views
NumPy is an open source tool with 11.7K GitHub stars and 3.83K GitHub forks. Here's a link to NumPy's open source repository on GitHub.
Pros of Denodo
Pros of NumPy
- Great for data analysis10
- Faster than list4
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What is Denodo?
What is NumPy?
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