AWS Lake Formation vs Azure Data Factory comparison

Amazon Web Services (AWS) and Microsoft are both solutions in the Cloud Data Warehouse category. Amazon Web Services (AWS) is ranked #8 with an average rating of 8.1, while Microsoft is ranked #2 with an average rating of 8.2. Amazon Web Services (AWS) holds a 4.6% mindshare in CDW, compared to Microsoft’s 5.3% mindshare. Additionally, 85% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 92% of Microsoft users who would recommend it.

AWS Lake Formation

Read 21 AWS Lake Formation reviews

1,610 Views
1,385 Comparison Views

85% willing to recommend

Azure Data Factory

Read 94 Azure Data Factory reviews

10,752 Views
1,720 Comparison Views

92% willing to recommend

AWS Lake Formation

Azure Data Factory

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 18, 2024

Azure Data Factory and AWS Lake Formation are competing cloud services in managing and orchestrating data workflows. Azure Data Factory seems to have an advantage with superior pricing and customer support but AWS Lake Formation's robust features justify its cost for those prioritizing comprehensive capabilities.

Features: Azure Data Factory offers entrenched integration options across various platforms, intuitive data mapping, and transformation capabilities with ease of use. AWS Lake Formation provides a focused system for setting up secure data lakes with automated data cataloging and fine-grained access control, establishing a strong data governance framework.

Room for Improvement: Azure Data Factory could improve its security features and offer more detailed monitoring capabilities. Expanding built-in connectors for even wider integration and enhancing real-time streaming support could also be beneficial. AWS Lake Formation might focus on streamlining its initial setup and improving user interface simplicity. Offering enhanced support for data analysis tools and additional automation features could further elevate its service.

Ease of Deployment and Customer Service: Azure Data Factory enables quick deployment with agility and receives praise for rapid customer service responses. AWS Lake Formation requires more setup time due to its emphasis on security and data governance, suitable for businesses needing extensive formulations but offers a thorough data management process.

Pricing and ROI: Azure Data Factory presents attractive pricing with low initial setup costs, promoting a favorable ROI for enterprises needing cost-effective solutions. AWS Lake Formation involves higher initial costs, which are balanced by its valuable data governance capabilities, rendering it appealing for businesses focusing on security and oversight.

To learn more, read our detailed AWS Lake Formation vs. Azure Data Factory Report (Updated: March 2026).

Buyer's Guide

AWS Lake Formation vs. Azure Data Factory

March 2026

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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

Ranking in other categories

No ranking in other categories

Azure Data Factory

Ranking in Cloud Data Warehouse

2nd

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (3rd)

Mindshare comparison

As of April 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.6%, down from 5.0% compared to the previous year. The mindshare of Azure Data Factory is 5.3%, down from 8.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	5.3%
AWS Lake Formation	4.6%
Other	90.1%

Cloud Data Warehouse

Featured Reviews

Ciro Baldim Guerra

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.

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KandaswamyMuthukrishnan

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.

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Quotes from Members

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

Pros

"In the shortest form, what I appreciated about AWS Lake Formation was that the schema definition and data cataloging were quite good."

"I can easily move data from cold storage to regular storage."

"Every solution has its pros and cons, however, for our purposes, this solution works quite well for us."

"AWS Lake Formation significantly improves the structure of the data mesh, making it superior to previous structures we used."

"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."

"A favorite feature of AWS Lake Formation is that it provides us with visibility into who has access to a particular table or database in Glue."

"AWS Lake Formation works hand in hand with other products."

"The main benefits that I have seen from using AWS Lake Formation are related to FinOps because you have control of your data and can track your costs since AWS Lake Formation is integrated into a unique platform, which is AWS Cloud Service."

More AWS Lake Formation pros

"Microsoft's technical support is responsive and quick to help."

"From what we have seen so far, the solution seems very stable."

"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."

"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface, so that eases the entire process."

"The user interface is very good. It makes me feel very comfortable when I am using the tool."

"The solution can scale very easily."

"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."

"The most valuable feature I have found at Azure Data Factory is the data flow function."

More Azure Data Factory pros

Cons

"I would appreciate online support, which I don't have access to in my corporation at the bank, so that is important."

"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."

"For the end-users, it's not as user-friendly as it could be."

"You need to have data experience to use the product."

"AWS Lake Formation's pricing could be cheaper."

"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."

"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."

"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."

More AWS Lake Formation cons

"The initial setup is not very straightforward."

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities."

"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"The one element of the solution that we have used and could be improved is the user interface."

"There is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration."

"Lacks a decent UI that would give us a view of the kinds of requests that come in."

More Azure Data Factory cons

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."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"Pricing appears to be reasonable in my opinion."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

"I would rate Data Factory's pricing nine out of ten."

"Pricing is comparable, it's somewhere in the middle."

"Data Factory is expensive."

More Azure Data Factory pricing and cost advice

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

12%

Computer Software Company

10%

Manufacturing Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	15

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	20
Large Enterprise	57

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...

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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...

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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...

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How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

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...

See all answers

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...

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Comparisons

Snowflake vs AWS Lake Formation

Compared 13% of the time

Databricks vs AWS Lake Formation

Compared 12% of the time

Amazon Redshift vs AWS Lake Formation

Compared 11% of the time

BigQuery vs AWS Lake Formation

Compared 8% of the time

Amazon EMR vs AWS Lake Formation

Compared 5% of the time

More AWS Lake Formation Competitors

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 5% of the time

Snowflake vs Azure Data Factory

Compared 5% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 3% of the time

Databricks vs Azure Data Factory

Compared 2% of the time

More Azure Data Factory Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

March 2026

Download AWS Lake Formation product report

Buyer's Guide

Azure Data Factory

March 2026

Download Azure Data Factory product report

Overview

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.

Amazon Web Services (AWS)

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

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

Buyer's Guide

AWS Lake Formation vs. Azure Data Factory

March 2026

Free Report: AWS Lake Formation vs. Azure Data Factory

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