Akamai Cloud Computing (formerly Linode) include scalable and accessible Linux cloud solutions and services. These products and services support developers and enterprises as they build, deploy, secure, and scale applications.
$5
per month
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
Akamai Cloud Computing
Amazon EMR (Elastic MapReduce)
Editions & Modules
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Offerings
Pricing Offerings
Akamai Cloud Computing
Amazon EMR
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
CPU, transfer, storage, and RAM are bundled into one price. Storage capacity can be increased with additional Block Storage or S3-compatible Object Storage. Instant Backups can be added with complete independence to the stack. Linode NodeBalancers ensure applications are available.
Akamai Connected Cloud Linode would be a good service to host a content delivery network (CDN) because of its edge network but I'd prefer not to use Akamai Connected Cloud Linode for tasks that need GPU power such as Machine Learning or Artificial Intelligence (AI) because Akamai Connected Cloud Linode lacks deep GPU compute compared to AWS or Google Cloud or Microsoft Azure
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.
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.
I've been with them a long time. They provide me with the capabilities I need coupled with knowledgeable support that's not pay-for-extra. However, if I move to a non-Linux OS, the level of support by necessity will drop off. I can still ask questions about the infrastructure but I my ability to ask about OS features will decrease.
Simple and clear, no BS interface. From a design perspective it's no Apple or Stripe, but it does what it needs without making me want to stick a fork in my eyes, like when being forced to use Azure, AWS or GCP.
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
There is very little planned downtime. Whenever planned downtime is necessary I'm always given lots of advanced notice and an explanation that I can pass along to my users that they'll understand. I really appreciate that Linode appreciates my commitment to reliable service to my users. It shows that they believe they've been successful when I'm successful.
Servers are well dimensioned and price performant. Of course one always wants more, so if they were to upgrade their hardware for the same price I'd consider moving more workloads. Networking - never had an issue. Hardware speeds - disks are fast and can grow to great size.
Support was excellent and fast. The documentation is extensive and helpful. I learned many things from their online documentation. I did not contact them by phone, but email took a day or less. Complex problems would probably need a service contract. I liked the friendly and polite tone of the support.
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.
We got kick started with an initial walkthrough along with some free credits. The initial walkthrough helped us to understand Linode's ecosystem and start our hands on with Linode. We tried out some apps from Marketplace initially with the free credits, which not only helped us understand Linode better, but also those apps. We had implemented many such apps to our customers with Linode
We're a small organization. The implementation of our Linode solution was trivial. Once I justified a cloud server to my bosses over a co-location -- the co-lo wasn't as fast as our linode server in load tests -- it was a matter of moving one Linux implementation to another. Trivial.
We switched to Linode from Namecheap due to poor uptime, and never had any issues with stability ever again after switching. We also cut our costs in half by switching. We compared Linode to DigitalOcean and Vultr, with the primary factor that caused us to go with Linode initially being their documentation. After using Linode for 3 years, their amazing support is another reason why we wouldn't consider anyone else at this point.
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.
Although I use only a fraction of their product offerings, the total set makes scalability an easy goal to shoot for. As I said, I have a few customers that use the services my Linode provides...and I like it that way. However, should I need to scale up, I can...without incurring any more cost than I need to.
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.
