

Amazon EMR and Amazon Redshift compete in the big data and data warehouse category. Amazon Redshift seems to have the upper hand due to its speed, scalability, and integration capabilities.
Features: Amazon EMR effectively manages big data, offers auto-scaling, and is cost-effective, integrating with Hadoop and ensuring minimal downtime. Amazon Redshift is known for its impressive query performance, scalability, and support for diverse data formats and BI tools, providing a powerful data warehouse experience.
Room for Improvement: Amazon EMR needs enhancements in web support, user interface, and setup complexity, as well as cost optimization and start-time performance issues. Amazon Redshift requires better snapshot restoring, DML features, and database compatibility, with some concerns over pricing and newer competitors.
Ease of Deployment and Customer Service: Amazon EMR primarily runs on the Public Cloud with strong technical support and quick responses through AWS channels. Amazon Redshift is deployable in Public, Private, and Hybrid Clouds but receives mixed customer service reviews, citing inconsistencies and slower response times.
Pricing and ROI: Both Amazon EMR and Redshift offer flexible pricing based on usage. EMR can be costlier due to its managed service model, whereas Redshift offers cloud-based cost-effectiveness despite higher initial setup costs. Both products deliver significant ROI, particularly for businesses transitioning from on-premise systems.
We earned back our investment in Amazon Redshift within the first year.
I would rate the technical support from Amazon as ten out of ten.
We get all call support, screen sharing support, and immediate support, so there are no problems.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
Documentation that allows anyone with prior knowledge of Redshift or SQL to resolve technical issues.
Whenever we need support, if there is an issue accessing stored data due to regional data center problems, the Amazon team is very helpful and provides optimal solutions quickly.
It's costly when you enable support.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
The scalability part needs improvement as the sizing requires trial and error.
We have successfully increased our storage space, which was a smooth process without server crashes before or after scaling.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2.
I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
Integration with AI features could elevate its capabilities and popularity.
Integration with AI could be a good improvement.
They should bring the entire ETL data management process into Amazon Redshift.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.
It's a pretty good price and reasonable for the product quality.
The cost of technical support is high.
The pricing of Amazon Redshift is expensive.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
Amazon EMR provides out-of-the-box solutions with Spark and Hive.
The features at Amazon EMR that I have found most valuable are fully customizable functions.
The specific features of Amazon Redshift that are beneficial for handling large data sets include fast retrieval due to cloud services and scalability, which allows us to retrieve data quickly.
Scalability is also a strong point; I can scale it however I want without any limitations.
Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.
| Product | Mindshare (%) |
|---|---|
| Amazon Redshift | 7.0% |
| Amazon EMR | 3.8% |
| Other | 89.2% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 21 |
| Large Enterprise | 29 |
Amazon EMR simplifies big data processing by offering integration with popular tools. It's scalable and cost-efficient, enabling fast processing while managing infrastructure effortlessly. It's designed for users aiming to streamline data workflows and leverage its batch processing capabilities effectively.
Amazon EMR is a managed service that provides robust features for big data processing. It integrates seamlessly with S3, EC2, Hive, and Spark to facilitate sophisticated data transformation tasks and infrastructure management. It allows organizations to run data lakes, Spark, and Hadoop clusters effortlessly, offering flexibility with on-demand execution and extensive scalability. The platform is valued for its strong processing speed and comprehensive security features, making it ideal for complex data engineering projects. It supports both batch processing and real-time workflows, designed to eliminate hardware management while maintaining cost efficiency and stability.
What are the key features of Amazon EMR?Amazon EMR is implemented by industries such as healthcare and tech processing for complex data tasks like building data lakes or financial data processing. It supports AI-driven analytics and data engineering projects, integrating with SageMaker for predictions and maintaining workflows in public health applications, allowing professionals in different fields to manage data pipelines, resource utilization, and job execution efficiently.
Amazon Redshift is a dynamic data warehousing and analytics platform offering scalability and seamless AWS integration for high-performance query processing and diverse data management.
Amazon Redshift provides robust data integration capabilities with AWS services like S3 and QuickSight, enabling efficient data warehousing and analytics. It is known for fast query performance due to its columnar storage and can handle diverse file formats. With a user-friendly SQL interface, Redshift supports data compression and offers a strong cost-performance ratio. Its secure VPC configurations and compatibility with data science tools enhance its functionality, although there is room for improving snapshot restoration, dynamic scaling, and processing large datasets.
What are the key features of Amazon Redshift?In industries, Amazon Redshift is essential for managing extensive datasets for business intelligence, operational insights, and reporting. It supports data integration from ERPs and S3, handles SQL queries for comprehensive analysis, and facilitates data storage and transformation. Companies use it for predictive modeling and connect with BI tools like Tableau and Power BI to derive actionable insights.
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