AWS Data Wrangler vs Metaflow

Overview

AWS Data Wrangler

Stacks7

Followers30

Votes0

Metaflow

Stacks16

Followers51

Votes0

GitHub Stars9.6K

Forks930

AWS Data Wrangler vs Metaflow: What are the differences?

Deployment Method: AWS Data Wrangler provides an easy deployment method using AWS CloudFormation, allowing users to quickly get started with their data processing tasks. On the other hand, Metaflow requires users to manually set up their infrastructure and manage dependencies, which can be more time-consuming and complex.
Workflow Management: Metaflow focuses on providing a rich workflow management framework that allows for easy tracking and visualization of workflows. In contrast, AWS Data Wrangler offers a more simplified approach to workflow management, making it easier for beginners to handle data processing tasks without the need for extensive workflow management expertise.
Integration with AWS Services: AWS Data Wrangler integrates seamlessly with various AWS services such as S3, Glue, Athena, and Redshift, making it a great choice for users already heavily invested in the AWS ecosystem. On the other hand, Metaflow is more agnostic and can be used with other cloud providers or on-premises solutions, providing more flexibility for users with diverse infrastructure requirements.
Community Support: Metaflow has a strong community of users and contributors, providing robust support and resources for users seeking assistance or looking to expand their knowledge. AWS Data Wrangler, being a relatively newer tool, may have a smaller community base, leading to potentially slower responses to queries or fewer resources available for troubleshooting.
Programming Language Compatibility: Metaflow primarily supports Python, making it a suitable choice for Python-centric data science workflows. AWS Data Wrangler, on the other hand, provides broader language support, including Python, Scala, and R, catering to users with diverse language preferences or requirements for their data processing tasks.
Pricing Model: AWS Data Wrangler is a managed service provided by AWS, which may incur costs based on usage and resource consumption. Metaflow, being an open-source framework, is free to use but may require additional resources for infrastructure maintenance and management, leading to potential hidden costs that users need to consider.

In Summary, AWS Data Wrangler and Metaflow differ in deployment method, workflow management, integration with AWS services, community support, programming language compatibility, and pricing model.

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Detailed Comparison

AWS Data Wrangler	Metaflow
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 a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
Writes in Parquet and CSV file formats; Utility belt to handle data on AWS	End-to-end ML Platform; Model with your favorite tools; Powered by the AWS cloud; Battle-hardened at Netflix
Statistics
GitHub Stars -	GitHub Stars 9.6K
GitHub Forks -	GitHub Forks 930
Stacks 7	Stacks 16
Followers 30	Followers 51
Votes 0	Votes 0
Integrations
Amazon Athena Apache Spark Apache Parquet PySpark	No integrations available

What are some alternatives to AWS Data Wrangler, Metaflow?

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

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.

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

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 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

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

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.

Dask

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

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.

StreamSets

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

Related Comparisons

AWS Data Wrangler vs Metaflow: What are the differences?

Deployment Method: AWS Data Wrangler provides an easy deployment method using AWS CloudFormation, allowing users to quickly get started with their data processing tasks. On the other hand, Metaflow requires users to manually set up their infrastructure and manage dependencies, which can be more time-consuming and complex.
Workflow Management: Metaflow focuses on providing a rich workflow management framework that allows for easy tracking and visualization of workflows. In contrast, AWS Data Wrangler offers a more simplified approach to workflow management, making it easier for beginners to handle data processing tasks without the need for extensive workflow management expertise.
Integration with AWS Services: AWS Data Wrangler integrates seamlessly with various AWS services such as S3, Glue, Athena, and Redshift, making it a great choice for users already heavily invested in the AWS ecosystem. On the other hand, Metaflow is more agnostic and can be used with other cloud providers or on-premises solutions, providing more flexibility for users with diverse infrastructure requirements.
Community Support: Metaflow has a strong community of users and contributors, providing robust support and resources for users seeking assistance or looking to expand their knowledge. AWS Data Wrangler, being a relatively newer tool, may have a smaller community base, leading to potentially slower responses to queries or fewer resources available for troubleshooting.
Programming Language Compatibility: Metaflow primarily supports Python, making it a suitable choice for Python-centric data science workflows. AWS Data Wrangler, on the other hand, provides broader language support, including Python, Scala, and R, catering to users with diverse language preferences or requirements for their data processing tasks.
Pricing Model: AWS Data Wrangler is a managed service provided by AWS, which may incur costs based on usage and resource consumption. Metaflow, being an open-source framework, is free to use but may require additional resources for infrastructure maintenance and management, leading to potential hidden costs that users need to consider.

In Summary, AWS Data Wrangler and Metaflow differ in deployment method, workflow management, integration with AWS services, community support, programming language compatibility, and pricing model.

AWS Data Wrangler vs Metaflow

Overview