Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. Users can launch instances with a variety of OSs, load them with custom application environments, manage network access permissions, and run images on multiple systems.
$0.01
per IP address with a running instance per hour on a pro rata basis
Amazon EMR
Score 8.9 out of 10
N/A
Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.
N/A
Pricing
Amazon Elastic Compute Cloud (EC2)
Amazon EMR (Elastic MapReduce)
Editions & Modules
Data Transfer
$0.00 - $0.09
per GB
On-Demand
$0.0042 - $6.528
per Hour
EBS-Optimized Instances
$0.005
per IP address with a running instance per hour on a pro rata basis
Carrier IP Addresses
$0.005 - $0.10
T4g Instances
$0.04
per vCPU-Hour Linux, RHEL, & SLES
T2, T3 Instances
$0.05 ($0.096)
per vCPU-Hour Linux, RHEL, & SLES (Windows)
No answers on this topic
Offerings
Pricing Offerings
Amazon Elastic Compute Cloud (EC2)
Amazon EMR
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Amazon Elastic Compute Cloud (EC2)
Amazon EMR (Elastic MapReduce)
Considered Both Products
Amazon Elastic Compute Cloud (EC2)
Verified User
Engineer
Chose Amazon Elastic Compute Cloud (EC2)
Amazon Elastic Compute Cloud (EC2) for me is the easy choice for servers. There are so many tools out there, specifically terraform and packer, that allow easy integration with EC2. It is great for any sized company. I have also used Google and Digital Ocean, but my first …
Having one of these enterprise edition license comes at its own costs. But, the flexibility to have the cluster spin up with the workbenches and code snippets on the same is really beneficial. Especially, if one had to move out of EMR and consider an option which reduces the …
Suitable for companies that are looking for performance at a competitive price, flexibility to switch instance type even with RI, flexibility to add-on IOPS, option to lower running cost with the regular introduction of new instance type that comes with higher performance but at a lower cost.
We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources.
EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy).
EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great.
The choices on AMIs, instance types and additional configuration can be overwhelming for any non-DevOps person.
The pricing information should be more clear (than only providing the hourly cost) when launching the instance. AWS DynamoDB gives an estimated monthly cost when creating tables, and I would love to see similar cost estimation showing on EC2 instances individually, as not all developers gets access to the actual bills.
The term for reserving instances are at least 12 months. With instance types changing so fast and better instances coming out every other day, it's really hard to commit to an existing instance type for 1 or more years at a time.
It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases.
Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR.
If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3.
You an start using EC2 instances immediately, is so easy and intuitive to start using them, EC2 has wizard to create the EC2 instances in the web browser or if you are code savvy you can create them with simple line in the CLI or using an SDK. Once you are comfortable using EC2, you can even automate the process.
Documentation is quite good and the product is regularly updated, so new features regularly come out. The setup is straightforward enough, especially once you have already established the overall platform infrastructure and the aws-cli APIs are easy enough to use. It would be nice to have some out-of-the-box integrations for checking logs and the Spark UI, rather than relying on know-how and digging through multiple levels to find the informations
AWS's support is good overall. Not outstanding, but better than average. We have had very little reason to engage with AWS support but in our limited experience, the staff has been knowledgeable, timely and helpful. The only negative is actually initiating a service request can be a bit of a pain.
I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good for Amazon EMR.
Amazon EC2 is super flexible compared to the PaaS offerings like Heroku Platform and Google App Engine since with Amazon EC2, we have access to the terminal. In terms of pricing, it's basically just the same as Google Compute Engine. The deciding factor is Amazon EC2's native integration with other AWS services since they're all in the same cloud platform.
Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
It reduced the need for heavy on-premises instances. Also, it completely eliminates maintenance of the machine. Their SLA criteria are also matching business needs. Overall IAAS is the best option when information is not so crucial to post on the cloud.
It makes both horizontal and vertical scaling really easy. This keeps your infrastructure up and running even while you are increasing the capacity or facing more traffic. This leads to having better customer satisfaction.
If you do not choose your instance type suitable for your business, it may incur lots of extra costs.
It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.
