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Amazon EC2 vs Amazon EMR: What are the differences?

Amazon EC2 (Elastic Compute Cloud) and Amazon EMR (Elastic MapReduce) are two popular services offered by Amazon Web Services (AWS) that provide computing capabilities. While both services offer scalable and cloud-based computing resources, there are key differences between them.

  1. Use Case: Amazon EC2 is primarily designed for general-purpose computing tasks and allows users to create and manage virtual machines in the cloud. On the other hand, Amazon EMR is specifically tailored for big data processing and analytics, providing a managed Hadoop framework along with other popular big data processing tools.

  2. Managed Services: Amazon EC2 provides virtual servers that users have full control over, allowing them to install and configure any software and services they need. Amazon EMR, however, is a managed service that abstracts away the underlying infrastructure, automatically provisioning and configuring the required resources for big data processing tasks.

  3. Scalability and Auto-scaling: Amazon EC2 offers manual scaling, where users can manually increase or decrease the number of instances based on their needs. In contrast, Amazon EMR supports both manual scaling and auto-scaling, enabling the cluster to automatically add or remove instances based on the workload.

  4. Instance Types: Amazon EC2 provides a wide range of instance types optimized for different use cases, such as compute-intensive, memory-intensive, or GPU-intensive workloads. Amazon EMR also offers a variety of instance types but focuses more on providing instances optimized for big data processing, such as instances with high-capacity storage or instances with powerful compute capabilities.

  5. Data Processing Tools: While Amazon EC2 provides the flexibility to install and use any data processing tools, Amazon EMR comes prepackaged with popular big data processing tools like Apache Hadoop, Spark, Hive, and Impala. This makes it easier for users to get started with big data processing without the need for manual installations and configurations.

  6. Pricing: Amazon EC2 pricing is based on the instance types, storage, data transfer, and other resources used. Amazon EMR pricing includes both EC2 instances and the additional cost for the EMR service itself. Additionally, Amazon EMR offers the option to use Spot Instances, providing significant cost savings for workloads that can tolerate interruptions.

In summary, while both Amazon EC2 and Amazon EMR are cloud-based computing services offered by AWS, they have distinct differences in terms of their use cases, managed services, scalability, instance types, data processing tools, and pricing models. Understanding these differences is crucial in choosing the right service for specific computing or big data processing needs.

Decisions about Amazon EC2 and Amazon EMR
Jerome/Zen Quah
Shared insights
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Amazon EC2Amazon EC2DigitalOceanDigitalOcean

DigitalOcean was where I began; its USD5/month is extremely competitive and the overall experience as highly user-friendly.

However, their offerings were lacking and integrating with other resources I had on AWS was getting more costly (due to transfer costs on AWS). Eventually I moved the entire project off DO's Droplets and onto AWS's EC2.

One may initially find the cost (w/o free tier) and interface of AWS daunting however with good planning you can achieve highly cost-efficient systems with savings plans, spot instances, etcetera.

Do not dive into AWS head-first! Seriously, don't. Stand back and read pricing documentation thoroughly. You can, not to the fault of AWS, easily go way overbudget. Your first action upon getting your AWS account should be to set up billing alarms for estimated and current bill totals.

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Craig Finch
Principal Consultant at Rootwork InfoTech · | 6 upvotes · 186.4K views

We first selected Google Cloud Platform about five years ago, because HIPAA compliance was significantly cheaper and easier on Google compared to AWS. We have stayed with Google Cloud because it provides an excellent command line tool for managing resources, and every resource has a well-designed, well-documented API. SDKs for most of these APIs are available for many popular languages. I have never worked with a cloud platform that's so amenable to automation. Google is also ahead of its competitors in Kubernetes support.

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Stephen Fox
Artificial Intelligence Fellow · | 2 upvotes · 189.5K views

GCE is much more user friendly than EC2, though Amazon has come a very long way since the early days (pre-2010's). This can be seen in how easy it is to edit the storage attached to an instance in GCE: it's under the instance details and is edited inline. In AWS you have to click the instance > click the storage block device (new screen) > click the edit option (new modal) > resize the volume > confirm (new model) then wait a very long time. Google's is nearly instant.

  • In both cases, the instance much be shut down.

There also the preference between "user burden-of-security" and automatic security: AWS goes for the former, GCE the latter.

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Most bioinformatics shops nowadays are hosting on AWS or Azure, since they have HIPAA tiers and offer enterprise SLA contracts. Meanwhile Heroku hasn't historically supported HIPAA. Rackspace and Google Cloud would be other hosting providers we would consider, but we just don't get requests for them. So, we mostly focus on AWS and Azure support.

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Pros of Amazon EC2
Pros of Amazon EMR
  • 647
    Quick and reliable cloud servers
  • 515
    Scalability
  • 393
    Easy management
  • 277
    Low cost
  • 271
    Auto-scaling
  • 89
    Market leader
  • 80
    Backed by amazon
  • 79
    Reliable
  • 67
    Free tier
  • 58
    Easy management, scalability
  • 13
    Flexible
  • 10
    Easy to Start
  • 9
    Elastic
  • 9
    Web-scale
  • 9
    Widely used
  • 7
    Node.js API
  • 5
    Industry Standard
  • 4
    Lots of configuration options
  • 2
    GPU instances
  • 1
    Simpler to understand and learn
  • 1
    Extremely simple to use
  • 1
    Amazing for individuals
  • 1
    All the Open Source CLI tools you could want.
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Massive data handling
  • 1
    Great support

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Cons of Amazon EC2
Cons of Amazon EMR
  • 13
    Ui could use a lot of work
  • 6
    High learning curve when compared to PaaS
  • 3
    Extremely poor CPU performance
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    What is Amazon EC2?

    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.

    What is Amazon EMR?

    It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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    What companies use Amazon EC2?
    What companies use Amazon EMR?
    See which teams inside your own company are using Amazon EC2 or Amazon EMR.
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    What are some alternatives to Amazon EC2 and Amazon EMR?
    Amazon LightSail
    Everything you need to jumpstart your project on AWS—compute, storage, and networking—for a low, predictable price. Launch a virtual private server with just a few clicks.
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    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
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    Amazon EC2 Container Service lets you launch and stop container-enabled applications with simple API calls, allows you to query the state of your cluster from a centralized service, and gives you access to many familiar Amazon EC2 features like security groups, EBS volumes and IAM roles.
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    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
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