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AWS Data Wrangler VS Celery

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

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  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.

Celery logo Celery

Celery helps innovative companies set up pre-order or custom crowdfunding campaigns anywhere.
  • AWS Data Wrangler Landing page
    Landing page //
    2023-08-29
  • Celery Landing page
    Landing page //
    2021-10-19

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

Celery videos

+ Add

Medical Medium Anthony William on the Dos and Don’ts of Celery Juice

More videos:

  • Review - Celery Juice Review: I Drank Celery Juice for 7 Days & This Is What Happened
  • Review - CELERY JUICE REVIEW | Medical medium's celery juice for 2 months | Honest Experience | Georgia Gibbs

Category Popularity

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

AWS Data Wrangler

Celery

Databases
100 100%
Databases
0% 0
Data Integration
0 0%
Data Integration
100% 100
Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Stream Processing
0 0%
Stream Processing
100% 100

User comments

Share your experience with using AWS Data Wrangler and Celery. 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.

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: 5 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

Celery mentions (0)

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

What are some alternatives?

When comparing AWS Data Wrangler and Celery, 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

Enqueue It - Easy and scalable solution for manage and execute background tasks seamlessly in .NET applications. It allows you to schedule, queue, and process your jobs and microservices efficiently.

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

Hangfire - An easy way to perform background processing in .NET and .NET Core applications.

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

Sidekiq - Sidekiq is a simple, efficient framework for background job processing in Ruby

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