Amazon EMR Reviews

• Availability
• Scalability
• Zero maintenance

Less expensive than in-house hosting, much more scalable when needed to process larger volumes of data.

Better monitoring, debugging, and stability are all needed.

I've used it for two years.

No issues encountered.

No issues encountered.

No issues encountered.

Customer Service:

Not consistent. Sometimes great, sometimes poor.

Technical Support:

Not consistent. Sometimes great, sometimes poor.

We used another vendors cloud solution to host our own Hadoop cluster. It wasn't scalable and productive enough,

It was straightforward.

We did it in-house.

It varies from vendor to vendor and depends on usage.

It varies from vendor to vendor and depends on usage.

There are several features that are most valuable for us--

• Hue
• Hive
• Spark
• S3

It's helped to automate processes. It's also provided quick deployment, and faster processing.

Quicker and offer automation deployment on multiple nodes.

I've used it for one year.

Sometimes there were issues.

Sometimes there were issues.

Sometimes there were issues.

Customer Service:

It's good.

Technical Support:

It's good.

No solution had been used previously, but we are using it alongside Hortonworks.

It was complex to configure.

It was done in-house.

We provide services for product implementation, so people looking for such products can contact me.

- A highly scalable platform to run vast amounts of data processing with very high efficiency. - Can be set to auto-scale up/down, as and when required. - Highly reliable platform with almost no downtime - Cost Effective - Can be used as a Web Management Console and a Web Services API. - Web consoles makes it very easy to run simple jobs. - Can be very easily integrated with Hadoop Clusters and HDFS distributed file systems. - Uses the in-house Amazon Elastic Cloud (EC2) and Amazon Simple Storage Service (Amazon S3) for providing a dynamic cloud storage facility.

- Setting up jobs for operations like data mining, web indexing, and machine learning is comparatively easier than log file analysis, financial analysis, etc . - For novice users, there is a bit of a steep learning curve, but things become much easier once you have the basics under your belt. - One of the lacking features is good web support. Though the web interface looks pretty decent, some of the basic features are missing. For example, you will find it a bit difficult to customize a particular map to reduce tasks, which involves a lot of customizations with regard to a given web indexing task. This involves extensive use of the underlying HDFS file system.

I've been using Amazon Elastic MapReduce for more than a year and found it to be a very useful tool. I was a bit hesitant to try this wonderful tool when I had started for the first time, but having previous expertise in a similar tool helped me grasp things at a faster pace. The bottom line is that if you have used some similar tools in the past, you are good to go. And, if you are new to the concept of a distributed task structure, it would be wise to spend a couple of minutes to get yourself acquainted with the MapReduce technology. This is my personal experience.

Amazon Elastic MapReduce is an IaaS platform and is capable of processing vast amounts of data on the cloud. It can do data processing with data sitting on different machines distributed across the cloud. It is based on the underlying technology of Hadoop and Hadoop Distributed File System for doing distributed data processing. It is comparatively cheaper than setting up your own distributed hardware platform. It is designed to grow up or scale down as per the requirement of the task, and all this is done automatically, without any manual intervention. No IT infrastructure management work is required as everything is over the cloud and managed by the service provider.

The web interface for managing all your cloud services is a bit patchy and needs improvement. The way the services and features are intermingled is quite difficult for a new user to get acquainted with. This requires a decent amount of time investment for learning the initial basics. Setting up map reduce tasks for financial analysis, file analysis is very difficult unlike other tasks like data mining etc.

Overall a good cloud infrastructure and takes the burden of managing the distributed hardware and can easily scale up and scale down as per the requirement of the task at hand. Very efficient in number crunching tasks using the map reduce technology as the tasks are distributed across multiple machines spread across the network. The web interface for managing cloud instances is a bit difficult to understand particularly for beginners and needs further improvement keeping in mind the ease of use for end users.