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AWS Data Wrangler

Move pandas/spark dataframes across AWS services
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What is AWS Data Wrangler?

It is a utility belt to handle data on AWS. It aims to fill a gap between AWS Analytics Services (Glue, Athena, EMR, Redshift) and the most popular Python data libraries (Pandas, Apache Spark).
AWS Data Wrangler is a tool in the Data Science Tools category of a tech stack.
AWS Data Wrangler is an open source tool with 450 GitHub stars and 39 GitHub forks. Here’s a link to AWS Data Wrangler's open source repository on GitHub

Why developers like AWS Data Wrangler?

Here’s a list of reasons why companies and developers use AWS Data Wrangler
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AWS Data Wrangler's Features

  • Writes in Parquet and CSV file formats
  • Utility belt to handle data on AWS

AWS Data Wrangler Alternatives & Comparisons

What are some alternatives to AWS Data Wrangler?
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
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.
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.
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
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.
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