Amazon EMR VS Datadog

Amazon EMR Reviews

Datadog Reviews

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

Datadog mentions (5)

• Send the logs of your Shuttle-powered backend to Datadog

  Ideally, if we had access to the underlying infrastructure, we could probably install the Datadog Agent and configure it to send our logs directly to Datadog, or even use AWS Lambda functions or Azure Event Hub + Azure Functions in case we were facing some specific cloud scenarios. - Source: dev.to / 7 months ago

• I wanted a self hosted alternative to Atlassian status page so I build my own application !

  Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 1 year ago

• Datadog on Kubernetes: Avoiding Common Pitfalls

  Datadog is a powerful monitoring and security platform that gives you visibility into end-to-end traces, application metrics, logs, and infrastructure. While Datadog has great documentation on their Kubernetes integration, we've observed that there's some missed nuance that leads to common pitfalls. - Source: dev.to / almost 3 years ago

• Post-DockerCon spam

  .. Is to see you email address being silently distributed to every single company that I've watched a talk from. And now suddenly get several promotional spam emails per day from some 4-5 different domains like instana.com, datadoghq.com, snyk.io, cockroachlabs.com (some of them send even multiple emails per day!). Source: almost 3 years ago

• Never write a UserService again

  We're commonly doing this with logging, using services such as Loggly or DataDog. We're using managed databases, be it on AWS, Heroku or database-vendor-specific solutions. We're storing binaries on S3. Externalising user authentication and authorization might be a good candidate as well. - Source: dev.to / about 3 years ago