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 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.
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.
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.
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 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.
The cost of technical support is high.
It's a pretty good price and reasonable for the product quality.
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.
The compute demand has decreased and it has reduced the cost significantly.
Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.
Scalability is the best feature of Amazon Redshift. Amazon Redshift handles scalability automatically, so we do not need to scale up or down; it is all managed by Redshift.
Scalability is also a strong point; I can scale it however I want without any limitations.
| Product | Market Share (%) |
|---|---|
| Amazon Redshift | 7.6% |
| Amazon EMR | 3.3% |
| Other | 89.1% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 5 |
| Large Enterprise | 11 |
| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 21 |
| Large Enterprise | 28 |
Amazon Redshift is a fully administered, petabyte-scale cloud-based data warehouse service. Users are able to begin with a minimal amount of gigabytes of data and can easily scale up to a petabyte or more as needed. This will enable them to utilize their own data to develop new intuitions on how to improve business processes and client relations.
Initially, users start to develop a data warehouse by initiating what is called an Amazon Redshift cluster or a set of nodes. Once the cluster has been provisioned, users can seamlessly upload data sets, and then begin to perform data analysis queries. Amazon Redshift delivers super-fast query performance, regardless of size, utilizing the exact SQL-based tools and BI applications that most users are already working with today.
The Amazon Redshift service performs all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.
Amazon Redshift Functionalities
Amazon Redshift has many valuable key functionalities. Some of its most useful functionalities include:
Reviews from Real Users
“Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there.” - Kundan A., Senior Consultant at Dynamic Elements AS
“With the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features. They have enriched and elaborate documentation that is helpful.”- Aishwarya K., Solution Architect at Capgemini
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