Amazon EMR vs Dremio comparison

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Amazon EMR

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Executive SummaryUpdated on Nov 9, 2025

Amazon EMR and Dremio both provide robust solutions in the data processing space. However, Amazon EMR holds a notable advantage in cost efficiency and scalability for handling large-scale deployments, whereas Dremio offers a more user-friendly platform with strong data management capabilities.

Features: Amazon EMR is renowned for its scalability, enabling users to efficiently handle large-scale data workloads. It seamlessly integrates with various big data tools including Hive and Spark and offers a reliable managed environment with auto-scaling capabilities. Dremio shines with its Virtual DataSet (VDS) feature, which provides visualization capabilities without altering original data. It also offers comprehensive data lineage and provenance, making it ideal for compliance and governance tasks.

Room for Improvement: Amazon EMR users often encounter complexities in setup and face challenges with the learning curve and cost management. Improvement is needed in areas like stability, version management, and customer support. Dremio users cite the need for better connector support, enhanced handling of complex queries, and scalability improvements. There are also calls for a more streamlined interface and expanded documentation.

Ease of Deployment and Customer Service: Amazon EMR is predominantly utilized in public cloud settings, benefiting from AWS's extensive infrastructure, although experiences with support are mixed regarding response times and cross-platform integrations. Dremio provides deployment flexibility across cloud and on-premises setups, offering versatile options, though customer support experiences vary, with some users noting inconsistencies in service quality.

Pricing and ROI: Amazon EMR is deemed cost-effective, especially for extensive deployments, with a pay-as-you-go model that demands careful cost monitoring. Users often report a high ROI, particularly when transitioning from on-premise systems. Dremio, though competitively priced against solutions like Snowflake, sometimes faces criticism for higher licensing costs needed for scaling. Regardless, it is recognized for offering good savings and ROI, notably for users migrating from traditional systems.

To learn more, read our detailed Amazon EMR vs. Dremio Report (Updated: December 2025).

Amazon EMR vs. Dremio

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

ROI

Sentiment score

4.8

Amazon EMR offers cost savings and ROI benefits, with some users experiencing up to 20% cost reduction and high returns.

Sentiment score

5.6

Dremio reduces manpower costs, enhances efficiency, and eliminates infrastructure concerns, improving operations by accessing multiple data sources.

No quotes available

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Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.

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SR BI developer at BRQ Digital Solutions

Customer Service

Sentiment score

7.9

Amazon EMR customer service varies, with generally responsive support despite reported delays and occasional gaps in integration assistance.

Sentiment score

5.2

Dremio's customer service is responsive and helpful, facing staffing challenges as demand grows, requiring more integrators for support.

We get all call support, screen sharing support, and immediate support, so there are no problems.

Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees

They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.

Lead AWS Data Engineer at Fission Labs

I would rate the technical support from Amazon as ten out of ten.

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Senior Technical Engineer at a transportation company with 5,001-10,000 employees

We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.

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SR BI developer at BRQ Digital Solutions

Scalability Issues

Sentiment score

7.4

Amazon EMR efficiently scales for businesses, offering customizable cluster options to manage diverse data sizes and enterprise demands.

Sentiment score

7.1

Dremio scales well, offering flexibility and built-in capabilities, though community users face scaling limits due to licensing.

Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.

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Lead AWS Data Engineer at Fission Labs

Dremio's scalability can handle growing data and user demands easily.

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SR BI developer at BRQ Digital Solutions

Internally, if it's on Docker or Kubernetes, scalability will be built into the system.

KamleshPant

Senior Software Architect at USEReady

Stability Issues

Sentiment score

7.7

Amazon EMR is praised for stability and reliability, with high ratings due to its configurability and robust features.

Sentiment score

7.2

Dremio is generally stable, scoring high ratings with occasional performance issues, especially with large datasets, requiring maintenance restarts.

Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.

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Lead AWS Data Engineer at Fission Labs

I rate Dremio a nine in terms of stability.

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SR BI developer at BRQ Digital Solutions

Room For Improvement

Amazon EMR users face challenges with customization, stability, onboarding, cost optimization, task speed, and demand enhanced integration and security.

Dremio struggles with Delta connector support, performance issues, SQL limitations, high costs, and fewer connectors than competitors.

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.

Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees

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.

Lead AWS Data Engineer at Fission Labs

I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.

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Senior Technical Engineer at a transportation company with 5,001-10,000 employees

Starburst comes with around 50 connectors now.

KamleshPant

Senior Software Architect at USEReady

It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.

For more quotes and insights, download the Dremio report

SR BI developer at BRQ Digital Solutions

Setup Cost

Amazon EMR pricing is variable, potentially costly, but users can manage expenses with strategic resource and instance management.

Dremio's pricing, though costly for scaling, is seen as valuable compared to competitors, requiring careful evaluation based on needs.

Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.

Lead AWS Data Engineer at Fission Labs

I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.

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Senior Technical Engineer at a transportation company with 5,001-10,000 employees

No quotes available

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Valuable Features

Amazon EMR offers scalable, cost-effective big data management with integration, flexibility, security, and seamless Hadoop and Spark processing.

Dremio offers efficient data management and visualization with seamless integration, native SQL, and role-based access control features.

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.

Lead AWS Data Engineer at Fission Labs

Amazon EMR provides out-of-the-box solutions with Spark and Hive.

Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees

The features at Amazon EMR that I have found most valuable are fully customizable functions.

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Senior Technical Engineer at a transportation company with 5,001-10,000 employees

Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.

Joao Silveira

Data Analyst at a insurance company with 501-1,000 employees

You just get the source, connect the data, get visualization, get connected, and do whatever you want.

KamleshPant

Senior Software Architect at USEReady

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Categories and Ranking

Amazon EMR

Ranking in Cloud Data Warehouse

13th

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Hadoop (4th)

Dremio

Ranking in Cloud Data Warehouse

5th

Average Rating

8.4

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Science Platforms (11th)

Mindshare comparison

As of December 2025, in the Cloud Data Warehouse category, the mindshare of Amazon EMR is 3.4%, up from 3.2% compared to the previous year. The mindshare of Dremio is 7.1%, down from 9.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
Dremio	7.1%
Amazon EMR	3.4%
Other	89.5%

Cloud Data Warehouse

Featured Reviews

Has simplified ETL workflows with on-demand processing but needs improved cost efficiency and visibility

Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees

I have used AWS Glue with S3 for making tables and databases, but regarding Amazon EMR, I do not remember much as we are currently using it very minimally. This is my observation: In EKS, we have had to deploy by ourselves because EKS does not provide the Hadoop framework, Spark, Hive, and everything, but we have completed all the deployment ourselves. Whereas Amazon EMR provides all these things. 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. In Qubole, the interface was very good. I could see many details because in Amazon EMR console, very few details are available. In Qubole, at one link, you can get all the details of what is happening, how the processes are running, and the cost decreased by using Qubole. I found Qubole more user-friendly and cost-effective. From the security point of view, we had to open some access rights to Qubole, which might be a drawback in comparison to Amazon EMR which is native to AWS.

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Has simplified complex data integration workflows and supported consistent reporting across multiple sources

SR BI developer at BRQ Digital Solutions

We also have a close relationship with the team that does the Dremio maintenance for the database, like upgrading the versions and they know about some specific problems we had in the past, such as a memory leak. We had a memory leak on some versions, which sometimes stopped the service. Since we are using Dremio installed like a server, not a SaaS solution, many times we need to stop and restart the service to clear all the cache and all that, and this is the thing I should add. I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement. I remember using some features in the past, like pivot tables, which proved to be really difficult, but I know this is a fault also for other vendors. Pivoting, transposing, and unpivoting are often not so good. CTEs also many times prove to be not so good, so I think these two main items could be improved significantly if they standardize them.

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Educational Organization

13%

Computer Software Company

Healthcare Company

Financial Services Firm

27%

Computer Software Company

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	5
Large Enterprise	12

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	5
Large Enterprise	5

Questions from the Community

What is your experience regarding pricing and costs for Amazon EMR?

Compared to others, Amazon seems efficient and is considered good for Big Data workloads. Costs are involved based on cluster resources, data volumes, EC2 ( /products/amazon-ec2-reviews ) instances...

What needs improvement with Amazon EMR?

What advice do you have for others considering Amazon EMR?

I am working on Amazon EMR but not extensively. Basically, our work is data transformation. Our pipelines work on that exclusively. We have Spark applications, and earlier, we used Amazon EMR exten...

What do you like most about Dremio?

Dremio allows querying the files I have on my block storage or object storage.

What is your experience regarding pricing and costs for Dremio?

I don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.

What needs improvement with Dremio?

I wouldn't say there is anything Dremio can be improved on. If I could change something, I would say many developers and programmers, when they are starting to work in this specific field or area, ...

Cloudera Distribution for Hadoop vs Amazon EMR

Comparisons

Snowflake vs Amazon EMR

Compared 28% of the time

Compared 17% of the time

Apache Spark vs Amazon EMR

Compared 10% of the time

Amazon Redshift vs Amazon EMR

Compared 9% of the time

Cloudera Data Platform vs Amazon EMR

Compared 7% of the time

More Amazon EMR Competitors

Databricks vs Dremio

Compared 35% of the time

Snowflake vs Dremio

Compared 16% of the time

Starburst Enterprise vs Dremio

Compared 7% of the time

Dataiku vs Dremio

Compared 6% of the time

Amazon SageMaker vs Dremio

Compared 5% of the time

More Dremio Competitors

Product Reports

Amazon EMR

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Download Dremio product report

Also Known As

Amazon Elastic MapReduce

Dremio AWS - BYOL

Overview

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.

Amazon Web Services (AWS)

Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.

Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.

What are Dremio's most valuable features?

Native Error Interfaces: Simplifies error detection and troubleshooting
Integration with Apache Airflow: Enhances workflow automation capabilities
Scalability: Effortlessly handles growing data demands
Role-Based Access Management: Provides secure data access control
Data Reflection: Optimizes query performance automatically
Query Federation: Supports queries across multiple endpoints seamlessly
Data Cataloging and Virtualization: Offers streamlined data organization

What benefits and ROI should users look for?

Reduced Data Storage Costs: Efficiently manages data to minimize storage expenses
Improved Query Performance: Enhances processing speed for large datasets
Flexibility in Integration: Easily connects with storage solutions like Oracle and MySQL
Enhanced Data Management: Supports comprehensive data analysis workflows

Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.

Sample Customers

Yelp

UBS, TransUnion, Quantium, Daimler, OVH

Amazon EMR vs. Dremio