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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Pros of Amazon EC2
- Quick and reliable cloud servers647
- Scalability515
- Easy management393
- Low cost277
- Auto-scaling271
- Market leader89
- Backed by amazon80
- Reliable79
- Free tier67
- Easy management, scalability58
- Flexible13
- Easy to Start10
- Widely used9
- Web-scale9
- Elastic9
- Node.js API7
- Industry Standard5
- Lots of configuration options4
- GPU instances2
- Simpler to understand and learn1
- Extremely simple to use1
- Amazing for individuals1
- All the Open Source CLI tools you could want.1
Pros of Amazon EMR
- On demand processing power15
- Don't need to maintain Hadoop Cluster yourself12
- Hadoop Tools7
- Elastic6
- Backed by Amazon4
- Flexible3
- Economic - pay as you go, easy to use CLI and SDKs3
- Don't need a dedicated Ops group2
- Massive data handling1
- Great support1
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Cons of Amazon EC2
- Ui could use a lot of work13
- High learning curve when compared to PaaS6
- Extremely poor CPU performance3