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 #12 with an average rating of 7.6. Microsoft holds a 6.9% mindshare in CDW, compared to Oracle’s 5.1% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 94% of Oracle users who would recommend it.

Azure Data Factory

Read 92 Azure Data Factory reviews

14,334 Views
2,437 Comparison Views

92% willing to recommend

Oracle Autonomous Data Ware...

Read 19 Oracle Autonomous Data Warehouse reviews

2,118 Views
1,546 Comparison Views

94% 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: July 2025).

Buyer's Guide

Azure Data Factory vs. Oracle Autonomous Data Warehouse

July 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

6.9

Number of Reviews

Ranking in other categories

Data Integration (1st)

Oracle Autonomous Data Ware...

Ranking in Cloud Data Warehouse

12th

Average Rating

8.4

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2025, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 6.9%, down from 9.5% compared to the previous year. The mindshare of Oracle Autonomous Data Warehouse is 5.1%, up from 4.4% 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	6.9%
Oracle Autonomous Data Warehouse	5.1%
Other	88.0%

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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Kwajah Mohiuddin

Global Head of Architecture at a financial services firm with 1,001-5,000 employees

Provides self-repair features, but the setup is complex

We use the product for online applications. We use it in the financial industry The product has self-repair features. The tool tunes itself. It separates compute from storage. We can scale storage and compute separately. The setup is complex. Oracle is a complex tool. I have been using Oracle…

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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

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

"The solution can scale very easily."

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

"We haven't had any issues connecting it to other products."

"Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness."

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

"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."

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

More Azure Data Factory pros

"The product has self-repair features."

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

"It is an extremely scalable solution since you can dynamically change the resources as some other cloud solutions."

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

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

"The solution has a self-backup, so you don't need a DBA (database administrator) to do a backup."

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

"The product is easy to use."

More Oracle Autonomous Data Warehouse pros

Cons

"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

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

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

"When we initiated the cluster, it took some time to start the process."

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

"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."

"The product integration with advanced coding options could cater to users needing more customization."

More Azure Data Factory cons

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

"The solution lacks visibility options."

"One of the major problem is creating custom tablespace. The ADB serverless option doesn't support custom tablespace creation, which could cause issues during on-premise database migration that requires specifically named tablespace. There should be an option to create customized tablespace."

"I would like to see Application Express and Oracle R Enterprise fully supported, and I would like to see Oracle Data Mining supported as a front end."

"An improvement for us would be the inclusion of support for an internal IP, so we could use it directly with the VCN in Oracle Cloud."

"Ease of connectivity could be improved."

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

"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."

More Oracle Autonomous Data Warehouse cons

Pricing and Cost Advice

"The solution is cheap."

"The price you pay is determined by how much you use it."

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

"It's not particularly expensive."

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

"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."

"The licensing cost is included in the Synapse."

"Product is priced at the market standard."

More Azure Data Factory pricing and cost advice

"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 solution is expensive."

"You pay as you go, and you don't pay for services that you don't use."

"The licensing cost of the product can vary since you can integrate it very easily with other products or other cloud products...You pay as you use it, so it is not yearly or monthly payments to be made toward Oracle."

"The solution's cost is reasonable."

"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."

"In terms of architecture and pricing structure, I feel it is a little bit costly compared to Azure. It's fine compared to RedShift, but compared to Azure, it's a bit pricey when you calculate for one TB storage plus around five hours of reporting with the frequency of 1TB data. The cost adds up, making Oracle a bit expensive."

More Oracle Autonomous Data Warehouse pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Computer Software Company

12%

Manufacturing Company

Government

Computer Software Company

10%

Financial Services Firm

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	55

By reviewers
Company Size	Count
Small Business	6
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 do you like most about Oracle Autonomous Data Warehouse?

With Oracle Autonomous Data Warehouse, things are much simpler. Creating a structure, initializing the servers, extending the servers, those are all things that are very, very easy. That's the main...

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

Comparisons

Snowflake vs Azure Data Factory

Compared 8% of the time

Informatica PowerCenter vs Azure Data Factory

Compared 6% of the time

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

Compared 6% of the time

Palantir Foundry vs Azure Data Factory

Compared 5% of the time

FME vs Azure Data Factory

Compared 5% of the time

More Azure Data Factory Competitors

BigQuery vs Oracle Autonomous Data Warehouse

Compared 31% of the time

Microsoft Azure Synapse Analytics vs Oracle Autonomous Data Warehouse

Compared 23% of the time

Snowflake vs Oracle Autonomous Data Warehouse

Compared 14% of the time

Oracle Exadata vs Oracle Autonomous Data Warehouse

Compared 12% of the time

Databricks vs Oracle Autonomous Data Warehouse

Compared 9% of the time

More Oracle Autonomous Data Warehouse Competitors

Product Reports

Buyer's Guide

Azure Data Factory

September 2025

Download Azure Data Factory product report

Buyer's Guide

Oracle Autonomous Data Warehouse

August 2025

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

July 2025

Free Report: Azure Data Factory vs. Oracle Autonomous Data Warehouse

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