Azure Data Factory vs Oracle Autonomous Data Warehouse comparison

Microsoft and Oracle are both solutions in the Cloud Data Warehouse category. Microsoft is ranked #2 with an average rating of 8.2, while Oracle is ranked #11 with an average rating of 7.7. Microsoft holds a 5.7% mindshare in CDW, compared to Oracle’s 5.7% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 95% of Oracle users who would recommend it.

Azure Data Factory

Read 93 Azure Data Factory reviews

11,065 Views
1,770 Comparison Views

92% willing to recommend

Oracle Autonomous Data Ware...

Read 19 Oracle Autonomous Data Warehouse reviews

2,219 Views
1,664 Comparison Views

95% willing to recommend

Azure Data Factory

Oracle Autonomous Data Ware...

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Executive SummaryUpdated on Dec 18, 2024

Azure Data Factory and Oracle Autonomous Data Warehouse compete in the data management space. Azure Data Factory seems to have the upper hand with its integration capabilities and flexibility, while Oracle Autonomous Data Warehouse is notable for its automation and performance.

Features: Azure Data Factory offers data integration, built-in connectors for various sources, and a user-friendly drag-and-drop interface for creating data pipelines. It integrates well with Azure services and supports GitHub for data flow management. Oracle Autonomous Data Warehouse features self-patching, tuning, and scaling, reducing administrative work. It includes robust security and machine learning for seamless data management.

Room for Improvement: Azure Data Factory could enhance integration with Azure services, improve scheduling features, and offer better big data capabilities. Users request more connectors for systems like SAP and Oracle, as well as improved error feedback and pricing models. Oracle Autonomous Data Warehouse users seek better data migration support, storage flexibility, and an improved user interface for data mining and machine learning tasks, as well as better interconnectivity and documentation.

Ease of Deployment and Customer Service: Azure Data Factory is deployed on the public cloud, offering scalability with strong community support, though direct support can vary. Oracle Autonomous Data Warehouse offers public, private, and on-premises deployments, with responsive technical support, though some users desire more comprehensive options.

Pricing and ROI: Azure Data Factory uses a pay-as-you-go model, seen as cost-effective for data projects but posing challenges in cost prediction. Oracle Autonomous Data Warehouse is perceived as more expensive due to its features and automation, with trial offerings suggesting potential ROI through reduced infrastructure costs and improved data management.

To learn more, read our detailed Azure Data Factory vs. Oracle Autonomous Data Warehouse Report (Updated: December 2025).

Buyer's Guide

Azure Data Factory vs. Oracle Autonomous Data Warehouse

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

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)

Oracle Autonomous Data Ware...

Ranking in Cloud Data Warehouse

11th

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 5.7%, down from 9.0% compared to the previous year. The mindshare of Oracle Autonomous Data Warehouse is 5.7%, up from 4.6% 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	5.7%
Oracle Autonomous Data Warehouse	5.7%
Other	88.6%

Cloud Data Warehouse

Featured Reviews

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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reviewer1297710

IT Administrator at a manufacturing company with 1,001-5,000 employees

Analytics and data security needs are met but optimization requires improvement

We are using Oracle Autonomous Data Warehouse for analytics in my company The solution is used for analytics and it works for our data security needs. We continue to use it with satisfaction. Optimization should be better. The SQLs are sometimes very slow. I also noticed that Java is not…

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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

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

"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."

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

"The initial setup is very quick and easy."

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"I like that it's a monolithic data platform. This is why we propose these solutions."

"It is easy to deploy workflows and schedule jobs."

"It is beneficial that the solution is written with Spark as the back end."

More Azure Data Factory pros

"A very good integration feature that restricts access to unauthorized people."

"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."

"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."

"It is a stable and scalable solution."

"Self-patching and runs machine-learning across its logs all the time"

"The analytics have been very good. We've found them to be quite useful."

"The solution is used for analytics and it works for our data security needs."

"It provides Transparent Data Encryption (TDE) capabilities by default to address data security issues."

More Oracle Autonomous Data Warehouse pros

Cons

"We require Azure Data Factory to be able to connect to Google Analytics."

"It can improve from the perspective of active logging. It can provide active logging information."

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"The support and the documentation can be improved."

"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."

"The speed and performance need to be improved."

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

"I have encountered a problem with the integration with third-party solutions, particularly with SAP."

More Azure Data Factory cons

"A lot of the tools that were previously there have now been taken away."

"The initial setup was pretty complex. It was not easy."

"Ease of connectivity could be improved."

"The setup is complex."

"We are not satisfied with the technical support. Their understanding is lacking."

"The installation process is complex. Oracle can make the installation process better."

"It doesn't work well when you have unstructured data or you need online analytics. It is not as nice as Hadoop in these aspects."

"They should make the solution more user-friendly."

More Oracle Autonomous Data Warehouse cons

Pricing and Cost Advice

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"The pricing model is based on usage and is not cheap."

"The solution is cheap."

"Data Factory is expensive."

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

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

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

"Data Factory is affordable."

More Azure Data Factory pricing and cost advice

"ROI is high."

"We pay approximately $70,000 per month. The cost includes maintenance and support."

"The solution is expensive."

"Cloud solutions are cheaper, but in the long run, they may not be much cheaper. They certainly have a lower initial cost. The licensing is yearly, and it is based on the size of the hardware and the number of users."

"The solution's cost is reasonable."

"On a scale from one to ten, where one is a low price and ten is a high price, I rate the pricing an eight."

"The price depends on the configuration we choose."

"The cost is perfect with Oracle Universal credit."

More Oracle Autonomous Data Warehouse pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

11%

Manufacturing Company

Government

Manufacturing Company

10%

Media Company

Computer Software Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	57

By reviewers
Company Size	Count
Small Business	7
Midsize Enterprise	1
Large Enterprise	11

Questions from the Community

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...

See all answers

What is your experience regarding pricing and costs for Oracle Autonomous Data Warehouse?

We pay approximately $70,000 per month. The cost includes maintenance and support.

See all answers

What needs improvement with Oracle Autonomous Data Warehouse?

Optimization should be better. The SQLs are sometimes very slow. I also noticed that Java is not supported, which is not ideal.

See all answers

What is your primary use case for Oracle Autonomous Data Warehouse?

We are using Oracle Autonomous Data Warehouse for analytics in my company.

See all answers

Comparisons

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

Compared 6% of the time

Snowflake vs Azure Data Factory

Compared 6% of the time

FME vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

BigQuery vs Oracle Autonomous Data Warehouse

Compared 27% of the time

Databricks vs Oracle Autonomous Data Warehouse

Compared 16% of the time

Snowflake vs Oracle Autonomous Data Warehouse

Compared 15% of the time

Microsoft Azure Synapse Analytics vs Oracle Autonomous Data Warehouse

Compared 9% of the time

Oracle Exadata vs Oracle Autonomous Data Warehouse

Compared 7% of the time

More Oracle Autonomous Data Warehouse Competitors

Product Reports

Buyer's Guide

Azure Data Factory

January 2026

Download Azure Data Factory product report

Buyer's Guide

Oracle Autonomous Data Warehouse

January 2026

Download Oracle Autonomous Data Warehouse product report

Overview

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

Oracle Autonomous Data Warehouse is the world’s first and only autonomous database optimized for analytic workloads, including data marts, data warehouses, data lakes, and data lakehouses. With Autonomous Data Warehouse, data scientists, business analysts, and nonexperts can rapidly, easily, and cost-effectively discover business insights using data of any size and type. Built for the cloud and optimized using Oracle Exadata, Autonomous Data Warehouse benefits from faster performance and, according to an IDC report (PDF), lowers operational costs by an average of 63%.

Autonomous Database provides the foundation for a data lakehouse—a modern, open architecture that enables you to store, analyze, and understand all your data. The data lakehouse combines the power and richness of data warehouses with the breadth, flexibility, and low cost of popular open source data lake technologies. Access your data lakehouse through Autonomous Database using the world's most powerful and open SQL processing engine.

Oracle

Sample Customers

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

Hertz, TaylorMade Golf, Outront Media, Kingold, FSmart, Drop-Tank

Buyer's Guide

Azure Data Factory vs. Oracle Autonomous Data Warehouse

December 2025

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