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.
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
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
AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler. Source: over 2 years ago
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
Dask - Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love
Raygun - Raygun gives developers meaningful insights into problems affecting their applications. Discover issues - Understand the problem - Fix things faster.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Sentry.io - From error tracking to performance monitoring, developers can see what actually matters, solve quicker, and learn continuously about their applications - from the frontend to the backend.
Serverspec - Serverspec.github.com :
RabbitMQ - RabbitMQ is an open source message broker software.