Killed by Google VS Amazon EMR

>Google operates in China albeit via their HK domain. The Chinese government has access to the iCloud account of every Chinese Apple user. >They also had project DragonFly if you remember. Which never materialized. >The lesser of two evils is that one company doesn’t try to actively profile me (in order for their ads business to be better) with every piece of data it can find and forces me to share all possible... - Source: Hacker News / 7 days ago

Google operates in China albeit via their HK domain. They also had project DragonFly if you remember. The lesser of two evils is that one company doesn’t try to actively profile me (in order for their ads business to be better) with every piece of data it can find and forces me to share all possible data with them. Google is famously known to kill apps that are good and used by customers:... - Source: Hacker News / 7 days ago

> This is proved by countless “killed by Google” incidents.. Oh, the Google's Graveyard: https://killedbygoogle.com/. - Source: Hacker News / 7 days ago

And another one https://killedbygoogle.com/ Regular reminder that you’re asking for it. - Source: Hacker News / 10 days ago

I was already starting to feel a little cornered in the whole Google ecosystem and a bit limited with stuff like backups, vendor lock in, etc. (and you always have the obvious hanging over your head) and ultimately, I think I just find the mental model of a SQL database more intuitive compared to a NoSQL database. So I thought to myself; "the longer I leave it, the harder it'll be to make the switch". - Source: dev.to / 15 days ago

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago

I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago

This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago

Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago

Check out https://aws.amazon.com/emr/. Source: about 2 years ago