Highlight interesting links. Hide irrelevant ones. Remove whole websites from search results. Cyberpunk! Updated bookmark experience We all know what “bookmark” in every browser does, don’t we? What if I told you that bookmarks were insufficient and could be improved? I’m here to show you how :)
We usually add something valuable there, stuff we want to read later when we have time or not feeling lazy. It’s pretty usual for most of us that the “read later” folder is becoming “probably read later” and then finally “don’t know what’s here”.
How do we change it? Add the reminder to the link: set it up for the day you’re going to have some spare time, add a Mark to remind why you should do it. That’s the spirit!
What about valueless stuff? Links we don’t want to open again. “Why would we want it?” - you’re going to ask. Fair question. Have you ever been looking for an apartment? You should know then how it is to open the same apartment every day forgetting that you rejected it (and why). Once again you open the photo of the bathroom: “Ah, yeah, this bath is ugly...”. Don’t worry, you’re going to open the same room again tomorrow, the title photo is so promising…
Not with MarkALink! Add the link to the “Hide” group and forget about coming to this useless page henceforth. Whenever and wherever you see this link on the web, you’ll recognize it. And it’s only one of the cases where it could optimize your processes and save your time!
Based on our record, Amazon EMR seems to be more popular. It has been mentiond 10 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.
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: about 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
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