Amazon EMR vs Azure Data Factory comparison

Amazon Web Services (AWS) and Microsoft are both solutions in the Cloud Data Warehouse category. Amazon Web Services (AWS) is ranked #13 with an average rating of 7.7, while Microsoft is ranked #2 with an average rating of 8.2. Amazon Web Services (AWS) holds a 3.4% mindshare in CDW, compared to Microsoft’s 6.1% mindshare. Additionally, 83% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 92% of Microsoft users who would recommend it.

Amazon EMR

Read 25 Amazon EMR reviews

1,591 Views
827 Comparison Views

83% willing to recommend

Azure Data Factory

Read 93 Azure Data Factory reviews

11,582 Views
1,969 Comparison Views

92% willing to recommend

Amazon EMR

Azure Data Factory

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 18, 2024

Amazon EMR and Azure Data Factory compete in the data management solutions category. Amazon EMR seems to have the upper hand in user satisfaction, particularly in pricing, scalability, and support, although it is more expensive than Azure Data Factory.

Features: Amazon EMR offers significant scalability, integrates seamlessly with Hadoop clusters, and provides features like Spark and Hive ecosystem integration. It is designed for processing large data sets with minimal downtime. Azure Data Factory is known for robust data transformation capabilities, customizable workflows, and extensive pre-built connectors, making it ideal for ETL pipelines.

Room for Improvement: Amazon EMR could benefit from a simplified setup for newcomers, better monitoring and automation, and improved cost management. Azure Data Factory could enhance machine learning integrations, improve documentation, and streamline its user interface. Users also seek pricing transparency and support for real-time data processing.

Ease of Deployment and Customer Service: Both products are deployed on public and private clouds. Azure Data Factory offers hybrid cloud options for flexibility. Customer service for Amazon EMR receives praise for faster responses, while Azure Data Factory support has been noted as inconsistent, indicating room for improvement.

Pricing and ROI: Amazon EMR charges based on usage without licensing fees but can become costly due to infrastructure expenses, offering strong ROI for those transitioning from on-premise systems. Azure Data Factory uses a pay-as-you-go model, potentially becoming expensive with increased usage but generally cost-effective for smaller batches. Both solutions offer cost savings with different pricing structures and value realizations.

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

Buyer's Guide

Amazon EMR vs. Azure Data Factory

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

Azure Data Factory offers significant ROI, efficiency, and cost savings, with users highlighting benefits in data integration and migration.

No quotes available

For more quotes and insights, download the Amazon EMR report

Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.

Deena Thayalan

Data Engineer at Vthinktechnologies

For more quotes and insights, download the Azure Data Factory report

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

6.3

Azure Data Factory support is generally satisfactory, with responsive assistance and strong community resources enhancing user satisfaction.

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

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

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

reviewer1343079

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.

reviewer2043696

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

For more quotes and insights, download the Amazon EMR report

The technical support from Microsoft is rated an eight out of ten.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

The technical support is responsive and helpful

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

The technical support for Azure Data Factory is generally acceptable.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

For more quotes and insights, download the Azure Data Factory report

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

Azure Data Factory offers scalable cloud-based solutions for diverse operations, despite some third-party integration limitations and use case challenges.

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

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

For more quotes and insights, download the Amazon EMR report

Azure Data Factory is highly scalable.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

For more quotes and insights, download the Azure Data Factory report

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

7.8

Azure Data Factory is considered highly stable and reliable, though minor issues can occur, mostly in development environments.

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

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

For more quotes and insights, download the Amazon EMR report

The solution has a high level of stability, roughly a nine out of ten.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

For more quotes and insights, download the Azure Data Factory report

Room For Improvement

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

Azure Data Factory needs better integration, UI, documentation, data handling, pricing transparency, real-time processing, connectivity, and scheduling.

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.

reviewer1343079

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.

Mirza Mujtaba Baig

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.

reviewer2043696

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

For more quotes and insights, download the Amazon EMR report

Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

There is a problem with the integration with third-party solutions, particularly with SAP.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

For more quotes and insights, download the Azure Data Factory report

Setup Cost

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

Azure Data Factory's pricing is pay-as-you-go, with costs based on usage, offering competitive and cost-effective solutions.

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

Mirza Mujtaba Baig

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.

reviewer2043696

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

For more quotes and insights, download the Amazon EMR report

The pricing is cost-effective.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

It is considered cost-effective.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

For more quotes and insights, download the Azure Data Factory report

Valuable Features

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

Azure Data Factory offers scalable ETL processes with easy integration, user-friendly interface, and strong orchestration, security, and automation 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.

Mirza Mujtaba Baig

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.

reviewer1343079

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.

reviewer2043696

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

For more quotes and insights, download the Amazon EMR report

It connects to different sources out-of-the-box, making integration much easier.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.

Deena Thayalan

Data Engineer at Vthinktechnologies

Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

For more quotes and insights, download the Azure Data Factory report

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)

Azure Data Factory

Ranking in Cloud Data Warehouse

2nd

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (3rd)

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 Azure Data Factory is 6.1%, down from 9.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
Azure Data Factory	6.1%
Amazon EMR	3.4%
Other	90.5%

Cloud Data Warehouse

Featured Reviews

reviewer1343079

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

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

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.

Read full review

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

Integrates diverse data sources and streamlines ETL processes effectively

Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.

Read full review

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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

13%

Computer Software Company

12%

Manufacturing Company

Government

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	31
Midsize Enterprise	19
Large Enterprise	57

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...

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What needs improvement with Amazon EMR?

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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...

See all answers

How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

How does Azure Data Factory compare with Informatica PowerCenter?

Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...

See all answers

How does Azure Data Factory compare with Informatica Cloud Data Integration?

Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...

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Comparisons

Snowflake vs Amazon EMR

Compared 28% of the time

Cloudera Distribution for Hadoop vs Amazon EMR

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

Databricks vs Amazon EMR

Compared 4% of the time

More Amazon EMR Competitors

Snowflake vs Azure Data Factory

Compared 7% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 7% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 5% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 5% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

Product Reports

Buyer's Guide

Amazon EMR

December 2025

Download Amazon EMR product report

Buyer's Guide

Azure Data Factory

December 2025

Download Azure Data Factory product report

Also Known As

Amazon Elastic MapReduce

No data available

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)

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

Sample Customers

Yelp

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa

Buyer's Guide

Amazon EMR vs. Azure Data Factory

December 2025

Free Report: Amazon EMR vs. Azure Data Factory

Find out what your peers are saying about Amazon EMR vs. Azure Data Factory and other solutions. Updated: December 2025.

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