NewRelic Reviews

Amazon EMR Reviews

NewRelic mentions (83)

• How User-Centric Benchmark Testing Drives Exceptional Software Performance

  Google Lighthouse: An open-source tool for auditing performance, accessibility, and SEO of web pages. WebPageTest: Provides detailed insights into webpage load performance from different locations and browsers. New Relic: Offers real-time monitoring and performance analysis for web and mobile applications. Dynatrace: Provides automatic monitoring and performance analysis, with a focus on user experience metrics. - Source: dev.to / 1 day ago

• How to Setup a Project That Can Host Up to 1000 Users for Free

  Logging is useful to explain the non-exceptional behavior of the application. It provides an audit trail, that can be used to understand the activities of complex systems, to diagnose problems, and to gather performance-relevant data. Logentries is a powerful log management tool. It offers a nice graphic representation of log data through web UI. It integrates with New Relic, providing combined search across both... - Source: dev.to / 11 days ago

• Cleaning routines to keep your project without bugs

  *1. New Relic *— it’s a tool to check on the slow performance of your app. If any action of the user takes longer than usual, NewRelic will inform you about that. - Source: dev.to / about 1 month ago

• Caching in depth

  Tip: You can use tools like DataDog, perf (Linux), New Relic etc. To monitor cache performance. - Source: dev.to / 2 months ago

• How to Handle N+1 Queries for Optimal Database Performance in Django?

  Using APM tools like NewRelic, Sentry, Datadog, etc to monitor the performance of your application and while you're on it, they can help you identify N+1 queries. - Source: dev.to / 3 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: over 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: about 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