NATS.io is a connective technology for distributed systems and is a perfect fit to connect devices, edge, cloud or hybrid deployments. True multi-tenancy makes NATS ideal for SaaS and self-healing and scaling technology allows for topology changes anytime with zero downtime.
Based on our record, NATS should be more popular than Amazon EMR. It has been mentiond 63 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.
Several message brokers, such as NATS and database queues, are not supported by OpenTelemetry (OTel) SDKs. This article will guide you on how to use context propagation explicitly with these message queues. - Source: dev.to / 29 days ago
Https://nats.io/ (Tracker removed) > Connective Technology for Adaptive Edge & Distributed Systems > An Introduction to NATS - The first screencast I guess I don't need to know what it is. - Source: Hacker News / about 1 month ago
Pueue dumps the state of the queue to the disk as JSON every time the state changes, so when you have a lot of queued jobs this results in considerable disk io. I actually changed it to compress the state file via zstd which helped quite a bit but then eventually just moved on to running NATS [1] locally. [1] https://nats.io/. - Source: Hacker News / about 1 month ago
During our interview, we referred to NATS quite a few times! If you want to learn more about it, Sebastian suggests this tutorial series. - Source: dev.to / about 1 month ago
Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance... - Source: dev.to / 3 months 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
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