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 #7 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 5.3% mindshare in CDW, compared to Microsoft’s 6.1% 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

1,426 Views
1,340 Comparison Views

85% willing to recommend

Azure Data Factory

Read 93 Azure Data Factory reviews

11,582 Views
1,969 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: December 2025).

Buyer's Guide

AWS Lake Formation vs. Azure Data Factory

December 2025

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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

7th

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 December 2025, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 5.3%, up from 5.1% compared to the previous year. The mindshare of Azure Data Factory is 6.1%, down from 9.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Market Share Distribution
Product	Market Share (%)
Azure Data Factory	6.1%
AWS Lake Formation	5.3%
Other	88.6%

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

"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 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."

"There is no doubt that this place exceeded my expectations with its incredible ambiance, attentive service, and mouthwatering menu."

"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."

"We have observed measurable benefits in terms of cost savings, time savings, resource savings, and efficiency improvements in our workflows."

"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."

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

More AWS Lake Formation pros

"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."

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

"The scalability of the product is impressive."

"The data flows were beneficial, allowing us to perform multiple transformations."

"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."

"The security of the agent that is installed on-premises is very good."

"The most valuable features are data transformations."

"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."

More Azure Data Factory pros

Cons

"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."

"Despite following official documentation, configuration problems persisted, requiring weeks of support from multiple AWS engineers to resolve."

"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."

"If I could improve AWS Lake Formation, I would add more integrations with SageMaker."

"I haven't seen any measurable benefits from using AWS Lake Formation, such as time saving, resource saving, or efficiency improvements."

More AWS Lake Formation cons

"The user interface could use improvement. It's not a major issue but it's something that can be improved."

"There are limitations when processing more than one GD file."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."

"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."

"When the record fails, it's tough to identify and log."

"Azure Data Factory can improve by having support in the drivers for change data capture."

"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."

"If the user interface was more user friendly and there was better error feedback, it would be helpful."

More Azure Data Factory cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

"ADF is cheaper compared to AWS."

"The cost is based on the amount of data sets that we are ingesting."

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"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 solution's fees are based on a pay-per-minute use plus the amount of data required to process."

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

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

More Azure Data Factory pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Computer Software Company

Manufacturing Company

Retailer

Financial Services Firm

13%

Computer Software Company

12%

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	19
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.

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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 22% of the time

Amazon Redshift vs AWS Lake Formation

Compared 16% of the time

Databricks vs AWS Lake Formation

Compared 13% of the time

BigQuery vs AWS Lake Formation

Compared 10% of the time

Amazon EMR vs AWS Lake Formation

Compared 7% of the time

More AWS Lake Formation Competitors

Snowflake vs Azure Data Factory

Compared 7% of the time

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

Compared 7% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 5% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 5% of the time

Alteryx Designer vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

December 2025

Download AWS Lake Formation product report

Buyer's Guide

Azure Data Factory

December 2025

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

December 2025

Free Report: AWS Lake Formation vs. Azure Data Factory

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