Software Alternatives, Accelerators & Startups

VictorOps VS Amazon EMR

Compare VictorOps VS Amazon EMR and see what are their differences

VictorOps logo VictorOps

We make on-call suck less & help teams to solve problems faster.

Amazon EMR logo Amazon EMR

Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
  • VictorOps Landing page
    Landing page //
    2022-07-18
  • Amazon EMR Landing page
    Landing page //
    2023-04-02

VictorOps videos

VictorOps

More videos:

  • Review - VictorOps Suggested Responders and Machine Learning

Amazon EMR videos

Amazon EMR Masterclass

More videos:

  • Review - Deep Dive into What’s New in Amazon EMR - AWS Online Tech Talks
  • Tutorial - How to use Apache Hive and DynamoDB using Amazon EMR

Category Popularity

0-100% (relative to VictorOps and Amazon EMR)
Monitoring Tools
100 100%
0% 0
Data Dashboard
0 0%
100% 100
Incident Management
100 100%
0% 0
Big Data
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare VictorOps and Amazon EMR

VictorOps Reviews

Why VictorOps is the DevOps Alternative to PagerDuty
PagerDuty and VictorOps both support chat integrations (e.g., Slack and HipChat), yet only VictorOps offers bi-directional chat as well as native, in-app chat. Moreover, VictorOps uniquely integrates with GitHub and Jenkins to bring critical SDLC insights into the firefight.
Source: victorops.com

Amazon EMR Reviews

We have no reviews of Amazon EMR yet.
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Social recommendations and mentions

Based on our record, Amazon EMR seems to be more popular. It has been mentiond 10 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.

VictorOps mentions (0)

We have not tracked any mentions of VictorOps yet. Tracking of VictorOps recommendations started around Mar 2021.

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
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What are some alternatives?

When comparing VictorOps and Amazon EMR, you can also consider the following products

PagerDuty - Cloud based monitoring service

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

OpsGenie - Alerting and On-Call Management for Dev&Ops Teams

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Squadcast - Automate incident response, reduce downtime and enhance your tech teams’ delivery with a unified platform.

Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost