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 4.7% mindshare in CDW, compared to Microsoft’s 5.4% 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 March 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 4.7%, down from 5.0% compared to the previous year. The mindshare of Azure Data Factory is 5.4%, down from 8.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	5.4%
AWS Lake Formation	4.7%
Other	89.9%

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 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."

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

"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."

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

"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."

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

"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."

More AWS Lake Formation pros

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

"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."

"The solution has a good interface and the integration with GitHub is very useful."

"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."

"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."

"Azure Data Factory is a low code, no code platform, which is helpful."

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

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

More Azure Data Factory pros

Cons

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

"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."

"Information about the pricing, cost, and setup cost of the AWS solutions would be beneficial."

"Rather than creating an additional hundred tools, optimizing a tool to have a centralized location to do governance would be beneficial."

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

"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."

"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. This could be an area of improvement for them. It's too expensive to acquire their support."

More AWS Lake Formation cons

"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."

"I do not have any notes for improvement."

"Some of the optimization techniques are not scalable."

"I would like to see this time travel feature in Snowflake added to Azure Data Factory."

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

"Data Factory's monitorability could be better."

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

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

More Azure Data Factory cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

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

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"Data Factory is expensive."

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

"The solution is cheap."

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

"Data Factory is affordable."

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

Computer Software Company

Financial Services Firm

12%

Computer Software Company

11%

Manufacturing Company

10%

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.

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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 14% 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 6% 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

FME vs Azure Data Factory

Compared 4% of the time

Boomi iPaaS 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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