Pentaho Data Integration vs SciPy

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Pentaho Data Integration

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SciPy

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Pentaho Data Integration vs SciPy: What are the differences?

Developers describe Pentaho Data Integration as "Easy to Use With the Power to Integrate All Data Types". 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. On the other hand, SciPy is detailed as "Scientific Computing Tools for Python". 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.

Pentaho Data Integration and SciPy can be primarily classified as "Data Science" tools.

SciPy is an open source tool with 6.13K GitHub stars and 2.9K GitHub forks. Here's a link to SciPy's open source repository on GitHub.

According to the StackShare community, SciPy has a broader approval, being mentioned in 18 company stacks & 21 developers stacks; compared to Pentaho Data Integration, which is listed in 14 company stacks and 6 developer stacks.

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What is 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.

What is 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.

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What tools integrate with Pentaho Data Integration?
What tools integrate with SciPy?
    No integrations found
    What are some alternatives to Pentaho Data Integration and SciPy?
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    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
    NumPy
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    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.
    Dataform
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