Amazon EMR vs Databricks comparison

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Amazon EMR

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Executive Summary

We performed a comparison between Amazon EMR and Databricks based on real PeerSpot user reviews.

Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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

Amazon EMR vs. Databricks

December 2025

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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

6.4

Users experience mixed returns with Databricks, noting cost efficiency and scalability but facing challenges with measuring monetary gains.

No quotes available

For more quotes and insights, download the Amazon EMR report

For a lot of different tasks, including machine learning, it is a nice solution.

For more quotes and insights, download the Databricks report

Senior Data Engineer at a logistics company with 51-200 employees

When it comes to big data processing, I prefer Databricks over other solutions.

IshwarSukheja

Head CEO at bizmetric

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

7.0

Databricks customer service is praised for prompt, professional support, though some report delays; documentation helps many users.

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

Lead AWS Data Engineer at Fission Labs

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

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

For more quotes and insights, download the Amazon EMR report

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

Whenever we reach out, they respond promptly.

Senior Data Engineer at a logistics company with 51-200 employees

As of now, we are raising issues and they are providing solutions without any problems.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.

Lax Kas

Data Engineer at CRAFT Tech

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

7.4

Databricks is praised for its scalability, elasticity, and auto-scaling, providing high performance and flexibility across industries.

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

For more quotes and insights, download the Amazon EMR report

Lead AWS Data Engineer at Fission Labs

The sky's the limit with Databricks.

SimonRobinson

Governance And Engagement Lead

The patches have sometimes caused issues leading to our jobs being paused for about six hours.

Senior Data Engineer at a logistics company with 51-200 employees

Databricks is an easily scalable platform.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

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

7.6

Databricks is highly rated for reliability and efficiency, with minor issues quickly resolved, boasting strong user stability scores.

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

For more quotes and insights, download the Amazon EMR report

Lead AWS Data Engineer at Fission Labs

They release patches that sometimes break our code.

Senior Data Engineer at a logistics company with 51-200 employees

Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

Databricks is definitely a very stable product and reliable.

AvivCohen

Data Engineer at a tech vendor with 1,001-5,000 employees

Room For Improvement

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

Databricks needs better visualization, integration, clearer errors, UI enhancements, wider platform support, and improved documentation and usability.

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.

For more quotes and insights, download the Amazon EMR report

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

Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.

For more quotes and insights, download the Databricks report

Senior Data Engineer at a logistics company with 51-200 employees

We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

Setup Cost

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

Databricks' pricing varies widely based on usage and data volume, making it cost-effective yet potentially expensive for large-scale use.

Costs are involved based on cluster resources, data volumes, EC2 instances, instance sizes, Kubernetes, Docker services, storage, and data transfers.

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.

For more quotes and insights, download the Amazon EMR report

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

It is not a cheap solution.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.

SimonRobinson

Governance And Engagement Lead

Valuable Features

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

Databricks excels in ease of use, scalability, integration, and data governance, enhancing productivity and collaboration for data engineering.

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 functionality because we can deploy and get Spark functionality over Hadoop.

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.

For more quotes and insights, download the Amazon EMR report

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

Databricks' capability to process data in parallel enhances data processing speed.

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.

Lax Kas

Data Engineer at CRAFT Tech

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 (3rd)

Databricks

Ranking in Cloud Data Warehouse

9th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (1st), Data Management Platforms (DMP) (5th), Streaming Analytics (1st)

Mindshare comparison

As of January 2026, in the Cloud Data Warehouse category, the mindshare of Amazon EMR is 3.4%, up from 3.1% compared to the previous year. The mindshare of Databricks is 9.2%, up from 6.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
Databricks	9.2%
Amazon EMR	3.4%
Other	87.4%

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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SimonRobinson

Governance And Engagement Lead

Improved data governance has enabled sensitive data tracking but cost management still needs work

I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.

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881,082 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

22%

Educational Organization

13%

Computer Software Company

Healthcare Company

Financial Services Firm

18%

Manufacturing Company

Computer Software 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	25
Midsize Enterprise	12
Large Enterprise	56

Questions from the Community

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

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

What needs improvement with Amazon EMR?

I feel some lack of functionality in Amazon EMR. I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.

What advice do you have for others considering Amazon EMR?

I find it easy to integrate Amazon EMR with other AWS services like S3 or EC2 for data processing needs. I would rate this review as eight out of ten.

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

Cloudera Distribution for Hadoop vs Amazon EMR

Comparisons

Snowflake vs Amazon EMR

Compared 23% of the time

Compared 12% of the time

Apache Spark vs Amazon EMR

Compared 8% of the time

Amazon Redshift vs Amazon EMR

Compared 8% of the time

Microsoft Azure Synapse Analytics vs Amazon EMR

Compared 4% of the time

More Amazon EMR Competitors

Dataiku vs Databricks

Compared 7% of the time

Microsoft Power BI vs Databricks

Compared 6% of the time

Dremio vs Databricks

Compared 4% of the time

Informatica PowerCenter vs Databricks

Compared 4% of the time

Tableau Enterprise vs Databricks

Compared 3% of the time

More Databricks Competitors

Product Reports

Download Amazon EMR product report

Amazon EMR

January 2026

Download Databricks product report

January 2026

Also Known As

Amazon Elastic MapReduce

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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)

Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Sample Customers

Yelp

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware