AWS Data Wrangler
Dask
Apache Spark
Kafka
Serverspec
Celery
RSpec
OpenCV
Apache Airflow
ifttt
Microsoft Power Automate
Make.com
Pipefy
Pushwoosh
Kissflow
Process Street
AWS Data Wrangler Apache AirflowBased on our record, Apache Airflow seems to be a lot more popular than AWS Data Wrangler. While we know about 65 links to Apache Airflow, we've tracked only 4 mentions of AWS Data Wrangler. 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
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 2 months ago
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows. Source: 6 months ago
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring. - Source: dev.to / 6 months ago
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic! Source: 7 months ago
I agree there are many options in this space. Two others to consider: - https://airflow.apache.org/ - https://github.com/spotify/luigi There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file... - Source: Hacker News / 7 months ago
Dask - Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.
Kafka - Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.
Make.com - Tool for workflow automation (Former Integromat)