Dask might be a bit more popular than Apache Beam. We know about 16 links to it since March 2021 and only 14 links to Apache Beam. 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.
The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / 3 months ago
Apache Beam is one of many tools that you can use. Source: 5 months ago
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 1 year ago
Apache Beam: Batch/streaming data processing 🔗Link. - Source: dev.to / over 1 year ago
What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all. Source: over 1 year ago
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk. Source: about 2 years ago
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec. Source: about 2 years ago
Dask: Distributed data frames, machine learning and more. - Source: dev.to / over 2 years ago
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:. - Source: dev.to / over 2 years ago
I’m quite sure dask helps and has a pandas like api though will use disk and not just RAM. Source: over 2 years ago
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
NumPy - NumPy is the fundamental package for scientific computing with Python
Google BigQuery - A fully managed data warehouse for large-scale data analytics.