Amazon EMR Reviews

Amazon EMR's ROI varies across vendors and usage cases. Some businesses report significant savings and high returns, particularly those transitioning from on-premise systems, often seeing returns of at least two to one. Feedback suggests cost savings up to 20%. Many have not calculated ROI specifics, but general sentiment indicates a positive impact with savings observed by companies leveraging Amazon EMR in their operations.

Amazon EMR pricing experiences vary, emphasizing usage-based costs without fixed licensing fees. Users note potential high expenses driven by EC2 fees, infrastructure resources, and varying cluster usage. Despite being costly, some describe it as moderately priced and efficient for Big Data workloads. Cost optimization is achievable through strategic resource management and auto-scaling, but careful monitoring is crucial to avoid unexpected charges. Enterprise budgets range from $40,000 to over $1 million annually, influenced by service levels and support packages.

• "I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."
• "The product is not cheap, but it is not expensive."
• "Amazon EMR is not very expensive."

Amazon EMR is utilized to run Spark scripts and build data lakes by accessing various data sources. Users deploy it for tech processing, big data frameworks, managing data pipelines, and enabling serverless architectures. It supports AI projects with on-the-fly algorithm execution and handles millions of roles swiftly. EMR integrates with AWS services like SageMaker and Airflow for tasks such as predictive analysis, developing workflows, resource management, and analytics reporting.

Amazon EMR's customer service and support show mixed feedback. Some users report inconsistency, with responses ranging from excellent to poor. Others emphasize satisfaction, noting responsiveness, knowledgeable staff, and quick resolutions. AWS Premier Partners enjoy enhanced support. Some users face challenges with integration or slower response times. Despite these issues, many express happiness with support, rating it high. Technical support is available 24/7, assisting with technical and operational queries through a proactive approach.

Amazon EMR's initial setup is generally easy and quick, with some finding it more complex. While some users set up in about 30 minutes using AWS Console or Terraform, others experience longer timelines due to project requirements. Multi-factor authentication can pose challenges. Knowledge of big data simplifies the process, but documentation aids those unfamiliar. Larger teams handle setup efficiently, while scripts streamline deployment for production environments.

Amazon EMR demonstrates strong scalability, accommodating various business needs, from small teams to enterprises with thousands of users. Users can choose clusters based on requirements and apply auto-scaling for efficiency. Instances can be adjusted for memory, storage, and GPU, though some experience delays during resource allocation. It is widely adopted for processing large datasets. Monitoring and configuring clusters is key for optimal performance. Many leverage EMR for analytics and report generation, supporting vast data volumes seamlessly.

Many users find Amazon EMR stable and reliable. They note no significant issues but mention the occasional need for reconfiguration due to data changes. Some users highlight its high availability and fault tolerance. Features like monitoring, updates, and disaster recovery contribute to its stability. While some express satisfaction, a few suggest improvements, rating its stability between 8.5 and 9 out of 10. Stability is further supported by availability zones and failover capabilities.