Pandas vs Pentaho Data Integration

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Pandas

1.5K
1.1K
+ 1
22
Pentaho Data Integration

92
63
+ 1
0
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Pandas vs Pentaho Data Integration: What are the differences?

What is Pandas? High-performance, easy-to-use data structures and data analysis tools for the Python programming language. Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

What is Pentaho Data Integration? 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.

Pandas and Pentaho Data Integration can be categorized as "Data Science" tools.

Pandas is an open source tool with 20.7K GitHub stars and 8.16K GitHub forks. Here's a link to Pandas's open source repository on GitHub.

According to the StackShare community, Pandas has a broader approval, being mentioned in 110 company stacks & 341 developers stacks; compared to Pentaho Data Integration, which is listed in 14 company stacks and 6 developer stacks.

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Pros of Pandas
Pros of Pentaho Data Integration
  • 21
    Easy data frame management
  • 1
    Extensive file format compatibility
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    What is 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.

    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.

    Need advice about which tool to choose?Ask the StackShare community!

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    What tools integrate with Pandas?
    What tools integrate with Pentaho Data Integration?
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      What are some alternatives to Pandas and Pentaho Data Integration?
      Panda
      Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
      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.
      R Language
      R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      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.
      See all alternatives