Honeycomb might be a bit more popular than Amazon EMR. We know about 13 links to it since March 2021 and only 10 links to Amazon EMR. 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 haven't used anything else, but I'll gladly shill for https://honeycomb.io. - Source: Hacker News / 9 months ago
With all of this in place I went a step further and added Opentelemetry to track the stats of how often the routine was being triggered on Honeycomb. - Source: dev.to / about 1 year ago
Events can be used in many meaningful ways. The Event subsystem of B is pretty much a co-evolution of what honeycomb.io offers, but implemented completely differently - it is on bare-metal, and hence a lot cheaper. Because of that, B never subsampled, but always kept a full low of all events anywhere, no exceptions. Source: about 1 year ago
It should be noted that this is a very oblique ad for http://honeycomb.io. That in no way impugns the content of the post, and in fact, it's given the content of the post that I feel compelled to point out that, ultimately, this is an ad. Because what is sales and advertising, anyway? It's just a way to get you to buy a product, and you can't do that if you've never even heard about the product. I'm not currently... - Source: Hacker News / about 1 year ago
Very cool to see honeycomb.io is doing that. I'm about to embark on my distributed tracing learning journey, this makes me want to try honeycomb right away. Source: over 1 year 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: over 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
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Marathon - Marathon is a production-grade container orchestration platform for Mesosphere.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost