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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
8th
Average Rating
8.0
Reviews Sentiment
5.7
Number of Reviews
21
Ranking in other categories
No ranking in other categories
Azure Data Factory
Ranking in Cloud Data Warehouse
5th
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
96
Ranking in other categories
Data Integration (4th)
 

Mindshare comparison

As of June 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 3.8%, down from 5.4% compared to the previous year. The mindshare of Azure Data Factory is 5.3%, down from 7.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
Azure Data Factory5.3%
AWS Lake Formation3.8%
Other90.9%
Cloud Data Warehouse
 

Featured Reviews

Ciro Baldim Guerra - PeerSpot reviewer
Sr Analytics Engineer at Itau Unibanco S.A.
Has improved data governance by enabling clear ownership and structured access across teams
In my company, Itaú, we don't utilize all AWS offerings due to rigorous security measures. We operate approximately six to eight months behind other available services. I'm uncertain if gaps exist because of this limitation, though the system functions effectively for us. AWS Lake Formation offers column-level access control for databases, but we haven't implemented this feature either because it hasn't been approved by our compliance, governance, or security areas. In our current setup, everyone from my business unit uses the same consumer account. When access is requested for a table, everyone using that business unit account receives access. This could present a security concern, though it benefits new team members who automatically receive all necessary access permissions. However, I struggle to identify specific improvements needed in AWS Lake Formation.
KandaswamyMuthukrishnan - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The LF-Tag system with granular permissions was key to the project as a functionality of AWS Lake Formation."
"We use this to reduce latency from minutes to seconds, as we aim for real-time visibility into patient healthcare monitoring."
"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."
"AWS Lake Formation works hand in hand with other products."
"The features and capabilities of AWS Lake Formation that I have found most valuable are that it is really convenient to see all the different data assets that were configured and understand who has and what type of service has or does not have access to those services."
"We have observed measurable benefits in terms of cost savings, time savings, resource savings, and efficiency improvements in our workflows."
"Every solution has its pros and cons, however, for our purposes, this solution works quite well for us."
"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."
"We use the solution to move data from on-premises to the cloud."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."
"The reason that we implemented this product is for the full integration with the whole Azure environment."
"I can do everything I want with SSIS and Azure Data Factory."
"If you have Azure as a cloud service and you want to perform ETL then Azure Data Factory is a product that I can recommend."
"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."
"The solution can scale very easily."
 

Cons

"I think AWS Lake Formation could improve by enforcing the least privilege by design, moving from ad hoc grants to role-based access controls."
"In our current setup, everyone from my business unit uses the same consumer account. When access is requested for a table, everyone using that business unit account receives access. This could present a security concern, though it benefits new team members who automatically receive all necessary access permissions."
"Rather than creating an additional hundred tools, optimizing a tool to have a centralized location to do governance would be beneficial."
"I would appreciate online support, which I don't have access to in my corporation at the bank, so that is important."
"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."
"Despite following official documentation, configuration problems persisted, requiring weeks of support from multiple AWS engineers to resolve."
"Information about the pricing, cost, and setup cost of the AWS solutions would be beneficial."
"Data Factory's monitorability could be better."
"They should work on optimizing their licensing model and pricing structure."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"Technical support isn't the best, as it's a bit delayed at times. Whenever we need some urgent support, wherein we have to restart or something has stuck, it takes a bit of time."
"As far as customer service and support with Azure Data Factory, we are not always satisfied with the response time."
"The only challenge with Azure Data Factory is its exception-handling mechanism."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"Some prebuilt data source or data connection aspects are generic."
 

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"Data Factory is affordable."
"The licensing cost is included in the Synapse."
"Data Factory is expensive."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"Pricing appears to be reasonable in my opinion."
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Top Industries

By visitors reading reviews
Financial Services Firm
21%
Manufacturing Company
8%
Computer Software Company
6%
Retailer
6%
Financial Services Firm
12%
Computer Software Company
10%
Manufacturing Company
9%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise15
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise21
Large Enterprise63
 

Questions from the Community

What is your experience regarding pricing and costs for AWS Lake Formation?
I don't understand much about the pricing of AWS Lake Formation, but I know how to search for the cost of Glue jobs, and I use the calculator in Amazon. I use a tool to preview the cost based on th...
What needs improvement with AWS Lake Formation?
Regarding areas of AWS Lake Formation that could be improved or enhanced, I prefer not to answer, mainly because I do not believe that I would be the most valuable person to ask, as I have not used...
What is your primary use case for AWS Lake Formation?
My usual use cases for AWS Lake Formation involved securing and governing the data resources that we configured in AWS, but we did not use the analytics or machine learning capabilities specificall...
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: April 2026.
896,942 professionals have used our research since 2012.