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AWS Lake Formation vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

Review summaries and opinions

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

Categories and Ranking

AWS Lake Formation
Ranking in Cloud Data Warehouse
13th
Average Rating
7.6
Reviews Sentiment
6.9
Number of Reviews
8
Ranking in other categories
No ranking in other categories
Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
90
Ranking in other categories
Data Integration (1st)
 

Mindshare comparison

As of February 2025, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 5.1%, down from 6.6% compared to the previous year. The mindshare of Azure Data Factory is 9.1%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Ramesh Raghavan - PeerSpot reviewer
Centralized repository, offers various cataloging mechanisms for quick data retrieval but data governance capabilities could be better
There are a couple of areas for improvement with Lake Formation. One of the main challenges, especially when dealing with rich media content, like in MarTech (Marketing Technology) or ad agencies, is its versatility. Some clients feel that Lake Formation doesn’t meet their needs and they tend to prefer competitor products for those specific use cases. The second area for improvement is in data governance. Specifically, Lake Formation could enhance its capabilities in audit logs, real-time monitoring, and advanced data governance. This includes managing the entire data lineage—where the data originated, how it moves, and where it’s currently stored. The visibility of the data as it evolves is crucial, and that’s where more advanced governance capabilities would be beneficial.
Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.

Quotes from Members

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

Pros

"AWS Lake Formation works hand in hand with other products."
"The most important advantage in using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS. This is what I advise my clients."
"I can easily move data from cold storage to regular storage."
"The solution has many features that are applicable to events such as audits."
"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."
"We use AWS Lake Formation typically for the data warehouse."
"AWS Lake Formation lets you see all your data and tables on one screen."
"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"
"It is easy to integrate."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"It is beneficial that the solution is written with Spark as the back end."
"The trigger scheduling options are decently robust."
 

Cons

"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation. It would be useful if we could use automation to build images and use hardened images which are CIS compliant."
"AWS Lake Formation's pricing could be cheaper."
"In our experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS. This could be an area of improvement for them. It's too expensive to acquire their support."
"It falls short when it comes to more granular access control, such as cell-level or row-level entitlements which is a significant drawback for organizations that require precise control over who can access specific rows of data."
"If I could improve AWS Lake Formation, I would add more integrations with SageMaker."
"For the end-users, it's not as user-friendly as it could be."
"Lake Formation could enhance its capabilities in audit logs, real-time monitoring, and advanced data governance."
"You need to have data experience to use the product."
"Data Factory's performance during heavy data processing isn't great."
"Azure Data Factory's pricing in terms of utilization could be improved."
"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"Data Factory's monitorability could be better."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"I have not found any real shortcomings within the product."
"There are performance issues, particularly with the underlying compute, which should be configurable."
 

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."
"Pricing is comparable, it's somewhere in the middle."
"Pricing appears to be reasonable in my opinion."
"Data Factory is expensive."
"The price you pay is determined by how much you use it."
"Understanding the pricing model for Data Factory is quite complex."
"The solution's pricing is competitive."
"The cost is based on the amount of data sets that we are ingesting."
"This is a cost-effective solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
21%
Computer Software Company
13%
Manufacturing Company
11%
Government
5%
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 AWS Lake Formation?
It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services.
What is your experience regarding pricing and costs for AWS Lake Formation?
The pricing is expensive compared to OpenStack, but cheaper than other cloud environments. It's middle-of-the-road for regular storage yet very cost-effective when using Amazon Glacier for data.
What needs improvement with AWS Lake Formation?
If I could improve AWS Lake Formation, I would add more integrations with SageMaker. I would have built-in functions that provide statistics for the data when using the GUI, such as SageMaker Insig...
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...
 

Overview

 

Sample Customers

bp, Cerner, Expedia, Finra, HESS, intuit, Kellog's, Philips, TIME, workday
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 AWS Lake Formation vs. Azure Data Factory and other solutions. Updated: January 2025.
838,713 professionals have used our research since 2012.