Amazon EMR Reviews

We primarily use the solution for AWS services. For example, in a typical B2B project management, some coders are sequencing at different speeds and managing the overall ports from other coders. We deploy the solution on cloud.

We value the streamlined solution, and the project management is very streamlined using almost all the features.

We have had issues with the boolean mathematical operation in 2. X's big version is working in newer versions because the old version of the solution does not support it, which is a compatibility issue that can be improved. In addition, the legacy versions of the solution are not supported in the new versions.

We have been using this solution for six months.

I rate the scalability a seven out of ten, and approximately seven amazon users who are architects, project managers, and testers use the solution in our organization. We intend to gradually increase the usage in our organization by at least five to ten percent annually.

We have had a very good experience with customer service and support. I rate them a ten out of ten.

Positive

The initial setup is straightforward, and deployment takes between 15 and 20 days.

We implemented the solution through a vendor team.

We have not calculated our ROI.

The licensing costs are expensive. I rate them a seven out of ten, with one being the least expensive and ten being the most expensive.

I rate the solution an eight out of ten. The solution is good, but its compatibility with older versions can be improved. I recommend the solution to users considering implementing it in their organizations.

The product is deployed on cloud.

The product can be improved by automating their up-sizing and downsizing their cluster.

We have been using this solution for less than one year and are currently using one of the latest versions.

The solution is stable.

The solution is scalable.

I cannot rate customer service and support as we have not contacted them.

The initial setup was time-consuming, and deployment took approximately 30 minutes. In addition, one person was required for deployment and maintenance.

I cannot comment on licensing costs as I don't know the prices.

We evaluated IQ.

I rate this solution seven out of ten. The solution is good but can be improved by making it more user-friendly and easy to set up.

We usually use EMR with Spark and bring in Airflow or something. For example, when we grade big jobs from on-prem to the cloud, we do it in EMR with Spark. 

In the main application, we use Hive, Spark, and Flink, and there are some additional small tools. We have to do this job inside EMR itself and run from Airflow. We also use a service from Amazon, and we manage jobs through Airflow. There's lots of scheduling, et cetera, that happens. Before, we used Jenkins. Now, we also integrate from Jenkins to Airflow and its processes. It's easier to have everything connected via Amazon. 

The problem for us is it starts very slow. They need to improve the start time. If we use a long-running EMR, it costs a lot of money. However, when we start, for example, a job, if the job runs for one hour, it's normal as it starts in about ten minutes. If we want, for example, to run each five minutes, it's a problem if it takes ten minutes to start. It's a little bit weird that you cannot use the service within a short period. 

The support could be better.

We've been using the solution for about one year. 

We've had some issues here and there, however, for the most part, the solution is stable. 

The solution can scale quite well. It's not a problem.

A lot of teams use the solution. We have two or three teams that use it. 

Technical support could be faster. They don't respond as quickly as we would like. 

We used an EC2 service. We did manually manage it via our engineers. We also used Knime and looked into Kinesis. 

You don't need to deploy anything. You just start. You go to the computer and start using EMR.

Sometimes we found some strange things. We've had some exceptions. Various versions can be used, however.

I'm not sure about to cost of the licensing. However, it does depend on the integration, for example, and many other factors. 

I'd recommend the solution to others to try.

I'd rate it a six out of ten. 

We primarily use the solution for tech processing. 

It's made life very easy. Now, a lot of things are very automated. 

It is easy to manage. The applications are much easier as compared to others.

The solution is pretty simple to set up. 

It's stable and reliable.

The product can scale. 

The cost is increasing. We are looking into how we can optimize the cost part of EMR. We're doing a comparison between Cloudera running on AWS and running AWS EMR.

We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part. 

I've been using this solution for a while now. It's been maybe a year or more. 

It's quite stable. There are no bugs or glitches and it doesn't crash or freeze. It's reliable. 

The product can scale. It's not a problem at all. 

Technical support is okay. The only challenge that we face is when case we integrate with other open-source solutions or products. We have issues, for example, with integrating ranges, and the VMR.

We are also using Hortonworks, which is now a part of Cloudera.

The initial setup was very easy. It's not overly complex or difficult. 

We can deploy the solution in a single day. It's very fast to get up and running. 

There's a team of ten people that can handle the setup and maintenance of the product.

We have a team that handles the initial setup.

The ROI would depend on the business case. I can't speak to an exact ROI. 

The price can get a bit high. We're looking for ways to reduce costs. 

Right now it costs us between $40,000 and $50,000 a month.

We are a customer and end-user.

I'd advise potential new users to give it a try if they have the requisite use cases. If it fits their use case, they should definitely go for EMR.

I'd rate the solution seven out of ten.

EMR is used to analyze data for projects where we wish to ingest multiple data sources into our analysts' data lakes. EMR is further used to process millions of roles within a fixed span of hours.

It gives us multiple options from Infra to the WES and different tools. It has a variety of options and support systems. Plus, it makes life easy for our developers, whether in the cloud or on-prem. Related DevOS installations are quick and easy to maintain.

Interdependencies with a third-party or open source solution should be improved. Modules and strategies should be better handled and notified early in advance. Maybe if AWS starts releasing AWS-certified or AWS-verified installations, that will generate even more confidence just like OpenJet, it'll add a specific version. 

I have been using Amazon EMR for three years. 

The solution is mostly stable with certain data and code related issues. 

Amazon EMR is a scalable solution. We have around twenty users, but they are engineering users. Outside, we have, our analytics product, which is being used for most of our clients. We have plans of upgrading the usage of the solution. 

The technical support team is good. The team is thorough with their knowledge and are quick to respond. 

The initial setup of Amazon EMR is straightforward if you know the basics and have knowledge of big data and architecture. The AWS documentation also helps with the deployment. Initially, the deployment takes a few hours and then it becomes easy. 

The maintenance depends on your application and specific requirements, but for deployment, you don't need a much bigger team, probably a team of one cloud guy should be enough.

The deployment can be done in-house. 

There is a small fee for the EMR system, but the major cost components are the underlying infrastructure resources that we actually use. 

My advice would be to do a dependency analysis to understand the limitations before planning to move in with Amazon EMR. I would rate the overall solution a six out of ten. 

A use case of this solution, for one of our clients with a large database of letters with addresses, is to predict if a person still lives at the listed address or if they have moved to another. We leverage EMR and SageMaker in AWS. 

EMR is cloud-based and managed through the cloud. 

One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR. 

Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana. 

I have been working with this solution for three years. 

This solution is pretty stable. 

It's managed services, so it's scalable as much as you wish. 

There are something like 40 to 50 people using EMR in my organization. 

We are an AWS Premier Partner, so we have all the necessary support and the ability to contact product teams. 

We didn't use any other products before implementing EMR. Some of our clients have Cloudera distributions, but we prefer EMR. 

The installation is straightforward because you can do it from the AWS Console or with Terraform. You can do it yourself. 

We implement this solution ourselves. On our team, we have admins, data engineers, DevOps engineers, and MLOps engineers. We have 40 or 50 data engineers. 

You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances. 

We have a range of clients in addition to the client with the large database of addresses. Another client is a large blockchain company and we do analytics for them, using Bare Metal and Hadoop, but not EMR. We're also doing Spark Streaming, Spark SQL, and some queries with Impala. We also have a company that enriches data from mobile companies, in terms of GAL locations of cell phones, with a variety of data from other sources to predict profitability.

I rate Amazon EMR an eight out of ten. It's continuously improving, and now it's possible to manage EMR directly from SageMaker Notebook. It's continuously evolving. I would recommend EMR to others because it's pretty straightforward, so onboarding doesn't take much time. 

Amazon EMR's most valuable features are processing speed and data storage capacity.

The product's features for storing data in static clusters could be better. It would be helpful if they released a beta version for limited users to know about the product.

I have been using Amazon EMR for one to two months.

It is a stable platform.

It is a scalable platform and suitable for enterprise businesses.

Amazon EMR's technical support services are good. We get an instant response and help over their chat portal.

The platform's initial setup process is easy. The time taken for deployment depends on specific project requirements. It takes approximately an hour to complete.

Amazon EMR's price is reasonable.

Amazon EMR is easy to use and easily adaptable to the environment. It reduces the cost of storing data in a static cluster.

I rate it a nine out of ten.

We are using Amazon EMR for data pipelines. We are using it to put our data into it and then we are transforming it.

We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot.

I have been using Amazon EMR for approximately one year.

Amazon EMR is reliable and stable.

Whenever we have issues we contact Amazon EMR support and we receive the responses. We are satisfied with the support.

The initial setup of Amazon EMR is easy.

The cost of Amazon EMR is very high.

My advice to others is that before implementing a solution they should look around. There are multiple solutions available and one might be a better fit for their use case.

I rate Amazon EMR an eight out of ten.