Amazon EMR Reviews

We use the solution to run spark script on our system for combination algorithms on our website. It's a Hadoop cluster to make the calculation to execute spark scripts. We have a cashback website and offer personalized recommendations to users and EMR is used to make the calculation by accessing user data. We also use this product for building a data lake using our numerous primary data sources. We've used EMR to make the latest version in the data lake. All data is stored in S3 bucket in a packet format. I'm Deputy CTO of the company. 

This tool is simple to use and it's really accessible to many dev teams. It's the best tool for hosts and it's really flexible and scalable which is necessary because we have a lot of data and some of our tasks take a lot of resources. 

The most complicated thing is configuring to the cluster and to ensure it's running correctly. You need to configure at least three Amazon policies to get authorization for all the instances. And if you're new on the system it's really complicated. It's something that could be simplified for users. For additional features, I'd like to see a better MLOps platform but it's possible that it's already in production. 

I've been using this solution for almost six years. 

We haven't had any problems with stability. 

The solution is scalable, you can choose the cluster with the instance you need depending on memory or storage or GPU as you need. There are about five users in the company. 

The initial setup is simple when you know the tool, but when you don't know the tool you need to look through the documentation. One of our team carried out deployment. We recently rebuilt our data lake and it took a day to get the right configuration.

We were on AWS for the web part so it was logical to take another AWS product, but today we are looking for an acceleration tool. The DevOps part takes a lot of time so we want to integrate with all the scope of MLOps and so we're looking at Databricks. 

I would recommend this solution and I rate it an eight out of 10. 

Public Cloud

• 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.

We're working to try the solution on a small-scale first. It's great that it's a solution that allows us to try it bit by bit.

The initial setup is pretty straightforward.

The stability of the product has been great overall.

Technical support has been knowledgeable and helpful.

The dashboard management could be better. Right now, it's lacking a bit. 

I'd like more of a remote connection between my computer and the solution.

We have multi-factor authentication, and at one point it was an issue due to the fact that I lost my phone. It stopped me from accessing the system.

We have to replicate all the infrastructure and we need to ensure that we have the scalability and to do so in production. We are hoping that Amazon will allow us to scale easily. However, we have not attempted to scale just yet.

I've been using the solution for a while, however, I can't specify the exact amount of time. It might have been approximately six to a year ago. I may have started using it in March 2020.

We haven't had any issues with Amazon in terms of stability. It's been good so far.

I can't exactly speak to the scalability of the solution just yet. All of the products we are using are in the development phase. We're testing everything out. Once we go into production we'll likely look more closely at scaling, however, at this point, it's not necessary. Therefore, I can't really discuss anything about it.

Technical support has been very good so far. We've been quite happy with them. They are knowledgeable and responsive and we are satisfied with the level of support provided to us.

The initial setup is pretty straightforward. It's not extremely complex.

However, it's difficult to reset the multi-factor authentication once it is set up. This was an issue for me when we lost the phone.

The price of the solution may be a bit more than other competitors, such as Microsoft.

I did look at other solutions before choosing Amazon. We did look at Microsoft as well, among others.

In general, I would rate the solution at a four out of ten.

Public Cloud

Amazon Web Services (AWS)

- 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.