Amazon EMR (Elastic MapReduce) Reviews and Ratings

Use Cases and Deployment Scope

Amazon EMR (Elastic MapReduce) is heavily used at my organization for most if not all data pipeline computations: we started by using EC2 instances, we then moved to EMR Serverless and we are actually completing the transition to EMR on EKS. In general we use it for long-running analysis (SQLs with a lot of JOINs) and overall for batch processing. From what I've seen, we use it with Spark under the hood.

Likelihood to Recommend

Based on my experience, Amazon EMR is well suited for companies with a good level of support on the Platform and Data Platform level, since it needs to be properly set up to avoid incurring in extra costs: it's quite easy to give more and more resources, so a job will eventually run but it's important to avoid extra costs. In general EMR on EC2 has been the most expensive of the EMR subproducts, while EMR on EKS has a good balance of giving enough resources to the jobs to run while maintaining costs low. The other recommendation is to use the latest versions of the EMR images, as otherwise the support from Amazon might not be very helpful.

Use Cases and Deployment Scope

The AWS stack is a big component of the majority of our work. When necessary, EMR is employed in a number of these settings. When we need to process a large amount of data across several EC2 servers, our DevOps team implements it. For our customers, EMR is attractive since it is far less expensive to adopt than alternative solutions, which means that the overall cost savings are substantial.

Likelihood to Recommend

You can use Amazon EMR if you wish to shift to the cloud and save money by using Apache Spark or Apache Hadoop on-premises. When the amount of work you have to handle data fluctuates a lot. Setting up flexible and scalable scenarios with AWS's EMR can assist you.

Use Cases and Deployment Scope

We use Amazon EMR (Elastic MapReduce) to run various types of algorithms related to health like calculation of body mass index, heart rate and similar parameters on vast amounts of data. We do this for developing a prototype of a health analysis device that users can wear on their body - something like a smart watch fitness tracker.

Likelihood to Recommend

<div>Scenarios where it is good:</div><div>1. Where speed is important, and there is a vast amount of data to process</div><div>2. Configuration setup needs to be fast </div><div>

</div><div>Scenarios where it is not good:</div><div>1. For small companies which do not have enough money</div><div>2. For one-off uses, since the ramp up curve is high

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Use Cases and Deployment Scope

For some clients, we have our product hosted on several AWS products, and when it comes to retrieving big volumes of data we use the Amazon EMR service. It has aided us in becoming more productive and saving time and effort. AWS is our go-to service for most of our needs,

Likelihood to Recommend

Our teams prefer using this service to deploy because it is simple to configure and scale even though it can be expensive at times. It also needs some training for new users to get familiar with all the functions and features. Experience matters a lot while using this platform.

Use Cases and Deployment Scope

On request transitory clusters for huge information handling. I like its accessibility completely different taken a toll tire makes it greatly flexible for distinctive scale clients. Can be pre-installed with any Huge information apparatuses like Hive, Start, Pig, etc. Nitty-gritty cluster observing makes a difference to track a few measurements, in turn, makes a difference to diminish fetched.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

Having our product hosted on various AWS EC2 instances for some clients and when it requires pulling large amounts of data and performing large transformations using client data, we would use the amazon EMR service to get that work done. The usage is limited to a few clients of our product rather than the entire client base.

Likelihood to Recommend

If someone is playing with a large dataset over AWS it's worth using it for a small kind of dataset it doesn't make sense as it's complex to manage.

Use Cases and Deployment Scope

We migrated the entire hadoop structure to Amazon EMR, the cost and maintenance are much better compared to other solutions on the market. We created a recommender system filter in big data. We needed a low runtime to meet our demand and we were able to get through the Amazon EMR.We migrated the entire hadoop structure to Amazon EMR, the cost and maintenance are much better compared to other solutions on the market. We have a lot of data science tasks, like calculating statistics between various math calculations to apply the business rules. Definitely one of the best services to work on bigdata.

Likelihood to Recommend

It provides a nice graphical user interface to manage and work with big data map reduction tasks instead of manual configuration with hadoop or cli.it saves a lot of time and effort.We create big data monitoring system filters.

It provides a good GUI to manage and handle big data map reduction tasks and its configuration saves a lot of time and effort.

Use Cases and Deployment Scope

I use Amazon EMR (Elastic Map Reduce) as a scalable platform to deploy my client solutions onto. It allows me to scale our solution elastically in the cloud and allows us to deal with any data size, volume, or complexity. It is very easy to configure and scale and it is my preferred platform to deploy to.

Likelihood to Recommend

Amazon EMR (Elastic Map Reduce) is ideally suited for organisations that need to provide a scalable platform for data processing. Amazon EMR is perfect to handle data volumes of any size or volume and it is easy to configure and spin up an EMR cluster quickly. If you are cost-focused you need to be careful as an EMR cluster can end up costing a lot of money if misconfigured.

Use Cases and Deployment Scope

To keep my review simple it is very convenient that AWS has a MapReduce tool as it was easy to deploy and test with our cloud setup. Also with AWS being well known it is easy to find staff who can use and set up a system and scale our solutions. Definitely an industry leader.

Likelihood to Recommend

When I need to process large data and meaningful information. But it is very flexible where I can scale based on the data size and how I want to analyze it. But still can improve for nontechnical users as there is some jargon to learn to get the most out of the solution.

Use Cases and Deployment Scope

We use it to process our data for real-time prediction for one of our AI models. This has tremendously reduced our effort and time. We have integrated this service with our product lines as well to process large amounts of contacts and generate an AI-based score to prioritize the contacts.

Likelihood to Recommend

It is a go-to software for experienced developers and for a company that wishes to process huge amounts of data at scale.