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Amazon EMR vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024
 

Categories and Ranking

Amazon EMR
Ranking in Cloud Data Warehouse
11th
Average Rating
7.8
Reviews Sentiment
7.2
Number of Reviews
22
Ranking in other categories
Hadoop (3rd)
Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
86
Ranking in other categories
Data Integration (1st)
 

Mindshare comparison

As of December 2024, in the Cloud Data Warehouse category, the mindshare of Amazon EMR is 4.4%, down from 4.6% compared to the previous year. The mindshare of Azure Data Factory is 12.8%, down from 13.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Prashant  Singh - PeerSpot reviewer
Easy to manage and reliable but the cost is hard to control
The cost is increasing. We are looking into how we can optimize the cost part of EMR. We're doing a comparison between Cloudera running on AWS and running AWS EMR. 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.
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

"The project management is very streamlined."
"The solution is pretty simple to set up."
"The initial setup is straightforward."
"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."
"The solution is scalable."
"It allows users to access the data through a web interface."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"Amazon EMR's most valuable features are processing speed and data storage capacity."
"The most valuable features are data transformations."
"It's extremely consistent."
"The solution has a good interface and the integration with GitHub is very useful."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"The scalability of the product is impressive."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
 

Cons

"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."
"The solution can become expensive if you are not careful."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"Spark jobs take longer on Amazon EMR compared to previous experiences."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"The product must add some of the latest technologies to provide more flexibility to the users."
"The initial setup was time-consuming."
"The problem for us is it starts very slow."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"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."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"The number of standard adaptors could be extended further."
"It does not appear to be as rich as other ETL tools. It has very limited capabilities."
"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"The setup and configuration process could be simplified."
 

Pricing and Cost Advice

"Amazon EMR's price is reasonable."
"There is no need to pay extra for third-party software."
"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 a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."
"The product is not cheap, but it is not expensive."
"The price of the solution is expensive."
"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."
"This is a cost-effective solution."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"Product is priced at the market standard."
"ADF is cheaper compared to AWS."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"Data Factory is expensive."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
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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?
The cost of Amazon EMR is a little bit expensive, especially considering the support package, which includes a gold package.
What needs improvement with Amazon EMR?
Spark jobs take longer on Amazon EMR compared to previous experiences. This aspect could be improved to make them more efficient.
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: December 2024.
824,067 professionals have used our research since 2012.