Amazon SQS VS Amazon EMR

Amazon SQS Reviews

Amazon EMR Reviews

Amazon SQS mentions (64)

• A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

  SQS - 1 million messaging queue requests. - Source: dev.to / 3 months ago

• Building Mature Content Detection for Mod Tools

  The last stage is productionizing the model. The goal of this phase is to create a system to process each image/video, gather the relevant features and inputs to the models, integrate the models into a hosting service, and relay the corresponding model predictions to downstream consumers like the MCF system. We used an existing Safety service, Content Classification Service, to implement the aforementioned system... Source: 5 months ago

• Testing Serverless Applications on AWS

  For context; the web application is built with React and TypeScript which makes calls to an AppSync API that makes use of the Lambda and DynamoDB datasources. We use Step Functions to orchestrate the flow of events for complex processing like purchasing and renewing policies, and we use S3 and SQS to process document workloads. - Source: dev.to / 6 months ago

• The Role of Queues in Building Efficient Distributed Applications

  Amazon SQS is a fully managed message queue service that provides a reliable and scalable solution for asynchronous messaging between distributed components and microservices. - Source: dev.to / 9 months ago

• Debugging SQS subscription issues to topics

  The key service that publishes messages to its subscribers is Simple Notification Service (SNS). We can add multiple different subscribers to a topic, for example, email addresses, phone numbers, or SQS queues. - Source: dev.to / 10 months ago

Amazon EMR mentions (10)

• 5 Best Practices For Data Integration To Boost ROI And Efficiency

  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

• What compute service i should use? Advice for a duck-tape kind of guy

  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

• Processing a large text file containing millions of records.

  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

• How to use Spark and Pandas to prepare big data

  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

• Beginner building a Hadoop cluster

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