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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
9th
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
6.8
Number of Reviews
95
Ranking in other categories
Data Integration (4th)
 

Mindshare comparison

As of May 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.1%, down from 5.3% compared to the previous year. The mindshare of Azure Data Factory is 5.3%, down from 7.9% 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 Formation4.1%
Other90.6%
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

"AWS Lake Formation has several valuable features that enhance data management, and one particularly beneficial aspect is how it facilitates better collaboration within data teams."
"We use AWS Lake Formation typically for the data warehouse."
"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."
"The most important advantage of using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS."
"AWS Lake Formation significantly improves the structure of the data mesh, making it superior to previous structures we used."
"We use this to reduce latency from minutes to seconds, as we aim for real-time visibility into patient healthcare monitoring."
"The LF-Tag system with granular permissions was key to the project as a functionality of AWS Lake Formation."
"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."
"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"So far, I'm quite happy with the solution overall."
"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"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."
"It's extremely consistent."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"It is beneficial that the solution is written with Spark as the back end."
 

Cons

"I haven't seen any measurable benefits from using AWS Lake Formation, such as time saving, resource saving, or efficiency improvements."
"For the end-users, it's not as user-friendly as it could be."
"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 experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS."
"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."
"The main challenge we faced with AWS Lake Formation was related to cross-account sharing. Granting access to other AWS accounts for tables or databases in a different AWS account was somewhat difficult."
"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."
"Despite following official documentation, configuration problems persisted, requiring weeks of support from multiple AWS engineers to resolve."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions."
"The speed and performance need to be improved."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API."
"The support and the documentation can be improved."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"Data Factory's performance during heavy data processing isn't great."
 

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."
"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 pricing model is based on usage and is not cheap."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"It's not particularly expensive."
"Understanding the pricing model for Data Factory is quite complex."
"I would not say that this product is overly expensive."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Manufacturing Company
7%
Retailer
6%
Government
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.
896,034 professionals have used our research since 2012.