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AWS Data Wrangler VS Hadoop

Compare AWS Data Wrangler VS Hadoop and see what are their differences

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

AWS Data Wrangler logo AWS Data Wrangler

Pandas on AWS. Contribute to awslabs/aws-data-wrangler development by creating an account on GitHub.

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing
  • AWS Data Wrangler Landing page
    Landing page //
    2023-08-29
  • Hadoop Landing page
    Landing page //
    2021-09-17

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

Hadoop videos

+ Add

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

Category Popularity

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

AWS Data Wrangler

Hadoop

Databases
15 15%
Databases
85% 85
Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Big Data
0 0%
Big Data
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0

User comments

Share your experience with using AWS Data Wrangler and Hadoop. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare AWS Data Wrangler and Hadoop

AWS Data Wrangler Reviews

We have no reviews of AWS Data Wrangler yet.
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Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
Source: www.datacamp.com
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
Source: www.analytixlabs.co.in
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Hadoop should be more popular than AWS Data Wrangler. It has been mentiond 15 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.

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: 6 months 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 2 years ago
  • Parquet files
    AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler. Source: over 2 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 3 years ago

Hadoop mentions (15)

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What are some alternatives?

When comparing AWS Data Wrangler and Hadoop, you can also consider the following products

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

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.

Kafka - Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Serverspec - Serverspec.github.com :

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