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 #5 with an average rating of 8.1. Amazon Web Services (AWS) holds a 4.4% mindshare in CDW, compared to Microsoft’s 5.2% 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

1,793 Views
1,506 Comparison Views

85% willing to recommend

Azure Data Factory

Read 94 Azure Data Factory reviews

11,377 Views
1,820 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: April 2026).

Buyer's Guide

AWS Lake Formation vs. Azure Data Factory

April 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

5th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (4th)

Mindshare comparison

As of April 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.4%, down from 5.1% compared to the previous year. The mindshare of Azure Data Factory is 5.2%, down from 8.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	5.2%
AWS Lake Formation	4.4%
Other	90.4%

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 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."

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

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

"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."

"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."

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

"The solution is quite good at handling analytics."

More AWS Lake Formation pros

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

"I value the integration capabilities with other platforms and software in Azure Data Factory the most."

"I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets."

"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."

"My only advice is that Azure Data Factory, particularly for data ingestion, is a good choice."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

"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."

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

More Azure Data Factory pros

Cons

"Lake Formation could enhance its capabilities in audit logs, real-time monitoring, and advanced data governance."

"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."

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

"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."

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

"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."

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

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

More AWS Lake Formation cons

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

"In the next release, it's important that some sort of scheduler for running tasks is added."

"Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"There is a problem with the integration with third-party solutions, particularly with SAP."

"The thing we missed most was data update, but this is now available as of two weeks ago."

"I wouldn't consider it to be stable since it fails at times."

"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."

More Azure Data Factory cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

"Data Factory is expensive."

"Product is priced at the market standard."

"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."

"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."

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

"I would not say that this product is overly expensive."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"Understanding the pricing model for Data Factory is quite complex."

More Azure Data Factory pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Manufacturing Company

Retailer

Government

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...

See all answers

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

Amazon Redshift vs AWS Lake Formation

Compared 12% of the time

Databricks vs AWS Lake Formation

Compared 12% of the time

BigQuery vs AWS Lake Formation

Compared 7% of the time

Amazon EMR vs AWS Lake Formation

Compared 5% of the time

More AWS Lake Formation Competitors

Snowflake vs Azure Data Factory

Compared 5% of the time

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

Compared 5% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

Alteryx Designer vs Azure Data Factory

Compared 2% of the time

More Azure Data Factory Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

April 2026

Download AWS Lake Formation product report

Buyer's Guide

Azure Data Factory

April 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

April 2026

Free Report: AWS Lake Formation vs. Azure Data Factory

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