AWS Lake Formation vs Azure Data Factory comparison

Why PeerSpot?

Amazon Web Services (AWS) and Microsoft are both solutions in the Cloud Data Warehouse category. Amazon Web Services (AWS) is ranked #4 with an average rating of 8.1, while Microsoft is ranked #8 with an average rating of 8.0. Amazon Web Services (AWS) holds a 3.3% mindshare in CDW, compared to Microsoft’s 5.0% 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

2,858 Views
2,201 Comparison Views

85% willing to recommend

Azure Data Factory

Read 97 Azure Data Factory reviews

16,078 Views
2,894 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: November 2026).

Buyer's Guide

AWS Lake Formation vs. Azure Data Factory

November 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

4th

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

8th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Integration (6th)

Mindshare comparison

As of November 2026, in the Cloud Data Warehouse category, the mindshare of AWS Lake Formation is 3.3%, down from 5.2% compared to the previous year. The mindshare of Azure Data Factory is 5.0%, down from 6.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
AWS Lake Formation	3.3%
Azure Data Factory	5.0%
Other	91.7%

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.

Read full review

Kunal Das

Test Engineer at Happiest Minds Technologies

Drag-and-drop pipelines have saved days of work and now automate data movement and backfilling

If the AI features were more improved so that I don't have to provide each and every detail, Azure Data Factory could be improved in a much better way by improving the AI features. For example, if I want to fetch any data from a raw source, I need to provide each and every detail. But if I am just uploading my raw data and if AI will sync with that data, it can analyze that data and give me proper suggestions on how that should be done in a proper way. Automatic suggestions could improve in a much better way. As I have mentioned, the AI features as well as more drag-and-drop activities could be improved. If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity. If I have used one activity, I shouldn't have to remember what activity should I use next. It should give auto-suggestions. That is why I have given a nine out of 10. Currently, I don't know about its governance and security, but in view of its improvement, I think Azure Data Factory should improve in these areas. As I already mentioned, the AI features should be improved. Also, the auto-suggestion features should also improve.

Read full review

Quotes from Members

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

Pros

"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."

"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."

"We use this to reduce latency from minutes to seconds, as we aim for real-time visibility into patient healthcare monitoring."

"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."

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

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

"The most valuable features of AWS Lake Formation were the access model itself, as it allows implementation of filters, Blueprints, and row-level and column-level security to mask data that shouldn't be accessed by certain entities, enabling granular control without exposing PII data."

More AWS Lake Formation pros

"We have been using drivers to connect to various data sets and consume data."

"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."

"Instead of individual people reviewing these files, we were able to automate the ingestion process, which saved a bunch of time and hours of repeated manual work."

"The most valuable part of this product is the ease of use, as it is easy to use and rather intuitive, and because it is easy to use, you can do things with it easily, making your work easier and therefore more valuable."

"We use the solution to move data from on-premises to the cloud."

"We have found the bulk load feature very valuable."

"Azure Data Factory is a good tool."

"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."

More Azure Data Factory pros

Cons

"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."

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

"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."

"AWS Lake Formation might not be easy to learn initially, especially for technical staff, due to various underlying options."

"There are areas that could be improved in AWS Lake Formation, and functionality that could be included in future releases."

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

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

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

More AWS Lake Formation cons

"I have not found any real shortcomings within the product."

"There aren't many third-party extensions or plugins available in the solution."

"When you raise an issue, sometimes the people who are available are unfamiliar with that particular technology, so they have to route the issue to the concerned person."

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

"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."

"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."

"But, I feel that if the usage extends beyond a certain threshold, it will start getting expensive."

"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."

More Azure Data Factory cons

Pricing and Cost Advice

"AWS Lake Formation is a bit expensive."

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

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

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

"I don't see a cost; it appears to be included in general support."

"The solution's pricing is competitive."

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

"The solution is cheap."

"The price is fair."

More Azure Data Factory pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Outsourcing Company

13%

Manufacturing Company

Construction Company

Manufacturing Company

11%

Financial Services Firm

10%

Outsourcing Company

Computer Software Company

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	21
Large Enterprise	64

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...

See all answers

Comparisons

Amazon Redshift vs AWS Lake Formation

Compared 12% of the time

Snowflake vs AWS Lake Formation

Compared 11% of the time

Databricks vs AWS Lake Formation

Compared 8% of the time

BigQuery vs AWS Lake Formation

Compared 6% of the time

Microsoft Azure Synapse Analytics vs AWS Lake Formation

Compared 6% of the time

More AWS Lake Formation Competitors

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

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

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

SnapLogic vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Product Reports

Buyer's Guide

AWS Lake Formation

November 2026

Download AWS Lake Formation product report

Buyer's Guide

Azure Data Factory

November 2026

Download Azure Data Factory product report

Overview

AWS Lake Formation streamlines data lake organization by integrating seamlessly with AWS services, ensuring robust access control, and facilitating effective data management. Its extensible framework and centralized data handling make it ideal for cloud environments that require scalability.

AWS Lake Formation offers comprehensive data synchronization with AWS, robust security through fine-grained policies, and seamless team collaboration tools. It centralizes data assets, supports analytics with Glue, and enhances data mesh structures. Its IAM integration supports efficient governance, with reliable data storage and retrieval backed by extensive documentation. While highly efficient within AWS ecosystems, Lake Formation faces integration challenges with third-party libraries, and users experience clutter in its interface. Aspects such as automation, policy management, and debugging are areas for improvement. Enhancements in orchestration, cross-account sharing, and integrations with services like SageMaker are user recommendations.

What are the key features of AWS Lake Formation?

Data Synchronization: Integrates with AWS services for seamless data management.
Security Policies: Implements fine-grained access control for secure data handling.
Team Collaboration: Facilitates cooperation through centralized data management.
Analytics Support: Enhances data processing with Glue integration.
Extensibility: IAM integration allows scalable governance.

What benefits or ROI can users expect?

Scalability: Efficiently manages expanding data volumes.
Cost Efficiency: Reduces cloud data management expenses.
Data Security: Ensures compliance with robust policies.
Comprehensive Support: Access to detailed documentation and AWS resources.

Organizations utilize AWS Lake Formation across various industries to build and manage data lakes, ensuring effective data governance and storage solutions. It helps integrate and transform data, control permissions, and support analytics. From e-commerce to healthcare, AWS Lake Formation delivers centralized data repositories for structured and unstructured data, securing information while providing scalable infrastructure and efficient resource management.

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

November 2026

Free Report: AWS Lake Formation vs. Azure Data Factory

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