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PySpark VS Anaconda

Compare PySpark VS Anaconda and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

PySpark logo PySpark

PySpark Tutorial - Apache Spark is written in Scala programming language. To support Python with Spark, Apache Spark community released a tool, PySpark. Using PySpark, you can wor

Anaconda logo Anaconda

Anaconda is the leading open data science platform powered by Python.
  • PySpark Landing page
    Landing page //
    2023-08-27
  • Anaconda Landing page
    Landing page //
    2023-09-22

PySpark features and specs

No features have been listed yet.

Anaconda features and specs

  • Comprehensive Distribution
    Anaconda provides a comprehensive distribution of Python and its associated packages, making it a one-stop solution for data science and machine learning projects.
  • Package Management
    Anaconda includes conda, a powerful package manager that allows easy installation, updating, and removal of packages and dependencies, which simplifies the environment management.
  • Environment Management
    Conda also supports environment management, enabling users to create isolated environments for different projects to avoid dependency conflicts.
  • Jupyter Notebooks Integration
    It provides built-in support for Jupyter Notebooks, which are widely used for data analysis, visualization, and prototyping in the data science community.
  • Cross-Platform Support
    Anaconda is available for Windows, macOS, and Linux, ensuring that users across different operating systems can leverage its capabilities.
  • Large Community and Support
    With a large and active community, Anaconda offers extensive online resources, tutorials, and a responsive support system.

Possible disadvantages of Anaconda

  • Large Installation Size
    Anaconda's comprehensive nature means it has a large installation size, which can be cumbersome for users with limited disk space.
  • Performance Overhead
    The extensive range of features and packages can lead to performance overhead compared to a more minimalistic Python setup.
  • Steeper Learning Curve
    Due to its vast array of tools and features, beginners might face a steeper learning curve compared to more minimalist distributions.
  • Potential Package Conflicts
    Although conda manages dependencies well, users can still encounter package conflicts, especially when working with packages outside the Anaconda repository.
  • Slower Package Availability
    Updates and new packages may be available later on conda compared to other Python package managers like pip, potentially delaying access to the latest features.

PySpark videos

+ Add

Data Wrangling with PySpark for Data Scientists Who Know Pandas - Andrew Ray

More videos:

  • Tutorial - Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Training | Edureka

Anaconda videos

+ Add

Anaconda - Good Bad Flicks

More videos:

  • Review - ANACONDA BAD MOVIE REVIEW | Double Toasted
  • Review - Anaconda - Good Bad or Bad Bad #23

Category Popularity

0-100% (relative to PySpark and Anaconda)

PySpark

Anaconda

Databases
100 100%
Databases
0% 0
Python IDE
0 0%
Python IDE
100% 100
Project Management
100 100%
Project Management
0% 0
Text Editors
0 0%
Text Editors
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare PySpark and Anaconda

PySpark Reviews

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Anaconda Reviews

The 16 Best Data Science and Machine Learning Platforms for 2021
Description: Anaconda offers its data science and machine learning capabilities via a number of different product editions. Its flagship product is Anaconda Enterprise, an open-source Python and R-focused platform. The tool enables you to perform data science and machine learning on Linux, Windows, and Mac OS. Anaconda allows users to download more than 1,500 Python and R...
Source: solutionsreview.com

What are some alternatives?

When comparing PySpark and Anaconda, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

PyCharm - Python & Django IDE with intelligent code completion, on-the-fly error checking, quick-fixes, and much more...

NumPy - NumPy is the fundamental package for scientific computing with Python

Dask - Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love

Spyder - The Scientific Python Development Environment

SciPy - SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. 

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