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  5. AWS Data Wrangler vs Mineo

AWS Data Wrangler vs Mineo

OverviewComparisonAlternatives

Overview

AWS Data Wrangler
AWS Data Wrangler
Stacks7
Followers30
Votes0
Mineo
Mineo
Stacks0
Followers1
Votes0

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Detailed Comparison

AWS Data Wrangler
AWS Data Wrangler
Mineo
Mineo

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).

It is the platform to explore your data, develop and deploy your Python supercharged Notebooks and track the quality of your data using Machine learning.

Writes in Parquet and CSV file formats; Utility belt to handle data on AWS
Develop, share and deploy your supercharged Python notebooks on a scalable platform; Automate and streamline your workflows with Python-based data pipelines that leverage the power of notebooks; Establish processes to measure and track the quality of your data over the time using machine learning
Statistics
Stacks
7
Stacks
0
Followers
30
Followers
1
Votes
0
Votes
0
Integrations
Amazon Athena
Amazon Athena
Apache Spark
Apache Spark
Apache Parquet
Apache Parquet
PySpark
PySpark
PingFederate
PingFederate
Microsoft SQL Server
Microsoft SQL Server
Python
Python
PostgreSQL
PostgreSQL
Amazon Redshift
Amazon Redshift
Clickhouse
Clickhouse
Oracle
Oracle
Slack
Slack
OpenID Connect
OpenID Connect

What are some alternatives to AWS Data Wrangler, Mineo?

Jupyter

Jupyter

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

Apache Zeppelin

Apache Zeppelin

A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.

Pandas

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

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.

Deepnote

Deepnote

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and present the polished assets to end users.

PyXLL

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

SciPy

SciPy

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.

Dataform

Dataform

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

PySpark

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.

Anaconda

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.

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