Pentaho Data Integration vs PySpark

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Pentaho Data Integration

77
47
+ 1
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PySpark

144
160
+ 1
0
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Pentaho Data Integration vs PySpark: 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, PySpark is detailed as "The Python API for Spark". It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

Pentaho Data Integration and PySpark belong to "Data Science Tools" category of the tech stack.

According to the StackShare community, Pentaho Data Integration has a broader approval, being mentioned in 14 company stacks & 6 developers stacks; compared to PySpark, which is listed in 8 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 PySpark?

It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

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What tools integrate with Pentaho Data Integration?
What tools integrate with PySpark?
    No integrations found

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    What are some alternatives to Pentaho Data Integration and PySpark?
    Talend
    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.
    Tableau
    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.
    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.
    NumPy
    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.
    Anaconda
    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
    See all alternatives