Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees
Real User
Top 20
Oct 6, 2025
We are using Amazon EMR for Spark as our platform involves a lot of ETL. We are using Amazon EMR for that, but in the last year, we have moved to Kubernetes, AWS EKS, where we are running Spark. Our Amazon EMR usage has been reduced as we have moved to the EKS side. We still have some of our pipelines running on Amazon EMR, but 95% of the pipelines are moved to the EKS.
I have been involved in using Amazon EMR ( /products/amazon-emr-reviews ) for data engineering projects, specifically for the submission of the jobs to the data clusters. I also focus on resource utilization, monitoring the job status, and managing memory resources.
I am a solution provider using Amazon EMR. Our main customer, American Express, pulls data from multiple sources, processes it, and provides analytics reporting. Amazon EMR supports AI-driven projects by allowing on-the-fly execution of algorithms to segregate data based on character properties.
We use the tool to maintain workflows on behalf of our customers. We use it in the medical field, particularly in public health and government healthcare. However, our primary role is maintaining and developing the solution for our clients rather than using it internally.
We use Amazon EMR for data processing. We read the data from S3 and then write the processed data back to S3. It allows users to access the data through a web interface.
Lead Data Engineer at Seven Lakes Enterprises, Inc.
Real User
Aug 15, 2023
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.
EMR Serverless is useful for online deployments that require a serverless architecture. We were previously using a server-based architecture, but we have since switched to serverless.
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 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.
Engineering Manager/Solution architect at a computer software company with 201-500 employees
Vendor
Dec 2, 2021
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.
Deputy CTO at a tech company with 51-200 employees
Real User
Jun 25, 2021
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.
Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.
We are using Amazon EMR for Spark as our platform involves a lot of ETL. We are using Amazon EMR for that, but in the last year, we have moved to Kubernetes, AWS EKS, where we are running Spark. Our Amazon EMR usage has been reduced as we have moved to the EKS side. We still have some of our pipelines running on Amazon EMR, but 95% of the pipelines are moved to the EKS.
I have been involved in using Amazon EMR ( /products/amazon-emr-reviews ) for data engineering projects, specifically for the submission of the jobs to the data clusters. I also focus on resource utilization, monitoring the job status, and managing memory resources.
I am a solution provider using Amazon EMR. Our main customer, American Express, pulls data from multiple sources, processes it, and provides analytics reporting. Amazon EMR supports AI-driven projects by allowing on-the-fly execution of algorithms to segregate data based on character properties.
We use the tool to maintain workflows on behalf of our customers. We use it in the medical field, particularly in public health and government healthcare. However, our primary role is maintaining and developing the solution for our clients rather than using it internally.
We use Amazon EMR to manage new data software like Hadoop.
We use Amazon EMR for data processing. We read the data from S3 and then write the processed data back to S3. It allows users to access the data through a web interface.
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.
EMR Serverless is useful for online deployments that require a serverless architecture. We were previously using a server-based architecture, but we have since switched to serverless.
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.
The product is deployed on cloud.
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.
We primarily use the solution for tech processing.
We are using Amazon EMR for data pipelines. We are using it to put our data into it and then we are transforming it.
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.
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.