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PySpark VS AWS Data Wrangler

Compare PySpark VS AWS Data Wrangler 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

AWS Data Wrangler logo AWS Data Wrangler

Pandas on AWS. Contribute to awslabs/aws-data-wrangler development by creating an account on GitHub.
  • PySpark Landing page
    Landing page //
    2023-08-27
  • AWS Data Wrangler Landing page
    Landing page //
    2023-08-29

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

AWS Data Wrangler videos

+ Add

AWS Tutorials - Introduction to AWS Data Wrangler

More videos:

  • Review - AWS Data Wrangler: Get Glue Catalog Table Description
  • Review - AWS Data Wrangler: Write Parquet to AWS S3

Category Popularity

0-100% (relative to PySpark and AWS Data Wrangler)

PySpark

AWS Data Wrangler

Project Management
100 100%
Project Management
0% 0
Databases
46 46%
Databases
54% 54
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Work Collaboration
100 100%
Work Collaboration
0% 0

User comments

Share your experience with using PySpark and AWS Data Wrangler. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, AWS Data Wrangler seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PySpark mentions (0)

We have not tracked any mentions of PySpark yet. Tracking of PySpark recommendations started around Mar 2021.

AWS Data Wrangler mentions (4)

  • Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
    I had no problem with awswrangler (https://github.com/aws/aws-sdk-pandas) and it supports reading and writing partitions which was really helpful and a few other optimizations that made it a great tool. Source: over 1 year ago
  • Redshift API vs. other ways to connect?
    Awslabs has developed their own package for this and given it's for their product, seem likely to maintain it. https://github.com/awslabs/aws-data-wrangler. Source: over 3 years ago
  • Parquet files
    AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler. Source: over 3 years ago
  • Go+: Go designed for data science
    Yep, agreed. Go is a great language for AWS Lambda type workflows. Python isn't as great (Python Lambda Layers built on Macs don't always work). AWS Data Wrangler (https://github.com/awslabs/aws-data-wrangler) provides pre-built layers, which is a work around, but something that's as portable as Go would be the best solution. - Source: Hacker News / about 4 years ago

What are some alternatives?

When comparing PySpark and AWS Data Wrangler, 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.

nSpek - nSpek, build forms and mobile checklists without coding or developers. Canadian Digital Inspections and Form Builder Provider for Heavy Industries such as Mining and Construction.

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

Serverspec - Serverspec.github.com :

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

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

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