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Amazon EMR vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Amazon EMR
Ranking in Cloud Data Warehouse
11th
Average Rating
7.8
Number of Reviews
21
Ranking in other categories
Hadoop (3rd)
Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
86
Ranking in other categories
Data Integration (1st)
 

Mindshare comparison

As of November 2024, in the Cloud Data Warehouse category, the mindshare of Amazon EMR is 4.5%, up from 4.5% compared to the previous year. The mindshare of Azure Data Factory is 12.9%, down from 13.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Quan Vu - PeerSpot reviewer
Provides efficient data processing features and has good scalability
We need to have a data pipeline tool to ensure consistent data processing for the initial setup. We create a framework, read the code, and execute it in a data catalog. The size of the maintenance team depends on the project and the use cases. Usually, one backup team of four or five DevOps executives takes care of the backend and database. We need to separate our environments into production and development. We use GitHub for source control, Jenkins for the deployment pipeline, and a standard CI/CD tool to deploy code changes into production. We need to develop a deployment framework so developers only need to provide the code for their projects. The underlying engine then deploys the code, reads it, addresses the EMR filter, executes it, and completes the data processing.
Thulani David Mngadi - PeerSpot reviewer
Data flow feature is valuable for data transformation tasks
The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

Quotes from Members

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

Pros

"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."
"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"Amazon EMR is a good solution that can be used to manage big data."
"Amazon EMR's most valuable features are processing speed and data storage capacity."
"It has a variety of options and support systems."
"The initial setup is pretty straightforward."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"The most important feature is that it can help you do the multi-threading concepts."
"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
"It is beneficial that the solution is written with Spark as the back end."
"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."
"We have found the bulk load feature very valuable."
"An excellent tool for pipeline orchestration."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
 

Cons

"The legacy versions of the solution are not supported in the new versions."
"The solution can become expensive if you are not careful."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"The product's features for storing data in static clusters could be better."
"There is room for improvement in pricing."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"The pricing model should be more transparent and available online."
"The speed and performance need to be improved."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"The thing we missed most was data update, but this is now available as of two weeks ago."
 

Pricing and Cost Advice

"You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances."
"The cost of Amazon EMR is very high."
"There is no need to pay extra for third-party software."
"The price of the solution is expensive."
"Amazon EMR is not very expensive."
"Amazon EMR's price is reasonable."
"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."
"I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"Pricing is comparable, it's somewhere in the middle."
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"The solution's pricing is competitive."
"The licensing cost is included in the Synapse."
"I would rate Data Factory's pricing nine out of ten."
"Pricing appears to be reasonable in my opinion."
"This is a cost-effective solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
13%
Manufacturing Company
9%
Educational Organization
7%
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon EMR?
Amazon EMR is a good solution that can be used to manage big data.
What is your experience regarding pricing and costs for Amazon EMR?
I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you...
What needs improvement with Amazon EMR?
The solution can become expensive if you are not careful.
How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
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...
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...
 

Also Known As

Amazon Elastic MapReduce
No data available
 

Overview

 

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
Find out what your peers are saying about Amazon EMR vs. Azure Data Factory and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.