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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

"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."
"In the shortest form, what I appreciated about AWS Lake Formation was that the schema definition and data cataloging were quite good."
"AWS Lake Formation works hand in hand with other products."
"The solution has many features that are applicable to events such as audits."
"The integration of AWS Lake Formation with the IAM for authentication and authorization is very good; I didn't have any problems in the setup and thought it was simple."
"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."
"AWS Lake Formation significantly improves the structure of the data mesh, making it superior to previous structures we used."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"The reason that we implemented this product is for the full integration with the whole Azure environment."
"It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse."
"I can do everything I want with SSIS and Azure Data Factory."
"The overall performance is quite good."
"Data Factory itself is great, it's pretty straightforward, you can easily add sources, join and lookup information, etc., and the ease of use is pretty good."
"The solution can scale very easily."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
 

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."
"Athena can be a bit clunky when writing queries, indicating a potential enhancement point for easier user interaction with query tools such as DataGrip using provided driver JARs."
"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."
"The initial onboarding process is challenging because creating a plan takes a month to a month and a half to build out."
"I think AWS Lake Formation could improve by enforcing the least privilege by design, moving from ad hoc grants to role-based access controls."
"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."
"You need to have data experience to use the product."
"For the end-users, it's not as user-friendly as it could be."
"The stability of Azure as a PaaS could be improved."
"There are limitations when processing more than one GD file."
"But, I feel that if the usage extends beyond a certain threshold, it will start getting expensive."
"I have not found any real shortcomings within the product."
"I do not have any notes for improvement."
"DataStage is easier to learn than Data Factory because it's more visual."
"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."
"I find that Azure Data Factory is still maturing, so there are issues."
 

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."
"ADF is cheaper compared to AWS."
"The solution's pricing is competitive."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"The licensing cost is included in the Synapse."
"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
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Top Industries

By visitors reading reviews
Financial Services Firm
21%
Manufacturing Company
7%
Construction Company
6%
Retailer
5%
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
9%
Construction Company
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: June 2026.
900,838 professionals have used our research since 2012.