Azure Data Factory vs Oracle Big Data Appliance comparison

Microsoft and Oracle are both solutions in the Cloud Data Warehouse category. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 75% of Oracle users who would recommend it.

Azure Data Factory

Read 97 Azure Data Factory reviews

14,099 Views
2,538 Comparison Views

92% willing to recommend

Oracle Big Data Appliance

Read 6 Oracle Big Data Appliance reviews

1,559 Views
1,232 Comparison Views

75% willing to recommend

Azure Data Factory

Oracle Big Data Appliance

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Executive Summary

We performed a comparison between Azure Data Factory and Oracle Big Data Appliance based on real PeerSpot user reviews.

Find out what your peers are saying about Snowflake Computing, Teradata, Google and others in Cloud Data Warehouse.

To learn more, read our detailed Cloud Data Warehouse Report (Updated: August 2026).

Buyer's Guide

Cloud Data Warehouse

August 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

Azure Data Factory

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Integration (5th), Cloud Data Warehouse (7th)

Oracle Big Data Appliance

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Warehouse (15th)

Featured Reviews

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.

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reviewer1031826

Analytics Lead at a tech vendor with 10,001+ employees

Platinum partnership leverages advanced storage and fosters seamless data integration

Areas of Oracle Big Data Appliance that can be improved include the pricing perspective because nowadays they have competition. We have so many technologies that have come to the market for less than even one-fourth or one-fifth of the Oracle price. For my side, the last time I worked with Oracle was in '21, '22. The price is a challenge that they have to look after. From the storage and data management perspective, I cannot recall anything that they have to improve, but they have to improve the visualization part significantly because they offer Oracle BI, which is an extremely not-matured application.

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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

"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."

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

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

"The stability of the Azure Data Factory is very good."

"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."

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

"Azure Data Factory was not difficult to deploy because it is a small area, so we completed it very quickly."

"Powerful but easy-to-use and intuitive."

More Azure Data Factory pros

"The most valuable feature is that it can be used in conjunction with Spark memory processing, and our processing time has gone to 20 minutes from what used to be two or three hours on the mainframe."

"The best thing about the product is that the end-user can build the reports by themselves without really knowing anything about databases."

"Because Big Data Appliance allows me to have a single source of truth, it means I have clean data that can be monetized and leveraged to gain more insights with real-time reports from the dashboard."

"Big Data responds to my needs."

"The best feature of Oracle Big Data Appliance, from a price perspective for the storage, is because they have technology called the ZFS technology; it offers huge storage with a decent price."

"This is a comprehensive solution that is easy to deploy."

"We could work on large unstructured datasets from different parts of the world on Facebook and Twitter to get better insights."

Cons

"Real-time replication is required, and this is not a simple task."

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

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."

"Some of the optimization techniques are not scalable."

"The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score."

"Technical support isn't the best, as it's a bit delayed at times. Whenever we need some urgent support, wherein we have to restart or something has stuck, it takes a bit of time."

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."

"There is one particular problem with Azure Data Factory. When you have a parent-to-child relationship and the child has one more relationship, creating a hierarchy situation, there are issues."

More Azure Data Factory cons

"We had to pursue several performance tuning measures to bring up the system to scale."

"It seems like the deployment of repositories has become more difficult in later versions of the product rather than easier."

"Areas of Oracle Big Data Appliance that can be improved include the pricing perspective because nowadays they have competition; we have so many technologies that have come to the market for less than even one-fourth or one-fifth of the Oracle price."

"We need to display the results on our website. I think there is another product that joins with it to give you the possibility."

"The product should be simplified for the average user."

"From a technical perspective, Big Data Appliance could be improved with more innovation in the AI and machine-learning parts instead of relying on Cloudera."

Pricing and Cost Advice

"Data Factory is expensive."

"The solution is cheap."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

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

"Pricing appears to be reasonable in my opinion."

"The pricing is a bit on the higher end."

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

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

More Azure Data Factory pricing and cost advice

"Oracle's prices are too high compared to others in the market."

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction Company

Financial Services Firm

20%

Manufacturing Company

13%

Comms Service Provider

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	64

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

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...

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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 needs improvement with Oracle Big Data Appliance?

See all answers

What is your primary use case for Oracle Big Data Appliance?

The typical use case for Oracle Big Data Appliance, which my clients use, is usually that the customer who has an Oracle application uses it because they can leverage the storage part. I don't need...

See all answers

Comparisons

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 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

SAP BW4HANA vs Oracle Big Data Appliance

Compared 12% of the time

Apache Hadoop vs Oracle Big Data Appliance

Compared 8% of the time

IBM Integrated Analytics System vs Oracle Big Data Appliance

Compared 8% of the time

Oracle Exadata vs Oracle Big Data Appliance

Compared 8% of the time

Snowflake vs Oracle Big Data Appliance

Compared 4% of the time

More Oracle Big Data Appliance Competitors

Product Reports

Buyer's Guide

Azure Data Factory

August 2026

Download Azure Data Factory product report

Buyer's Guide

Data Warehouse

August 2026

Download Oracle Big Data Appliance 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 Big Data Appliance offers a comprehensive platform integrating ZFS technology and supporting Spark, designed to boost performance with Hadoop distributions, ensuring data security and accessibility across multiple languages.

Oracle Big Data Appliance presents an adaptable solution facilitating data consolidation with robust storage technology, crucial for central data repositories. Its Spark in-memory processing significantly reduces time through seamless integration with Hadoop distributions like Hortonworks and Cloudera. Role-based security enhances organizational data access, while multi-language support meets diverse processing requirements. Although challenges exist in AI capabilities and visualization tool efficiency, the platform remains valuable for entity-wide deployment.

What key features does Oracle Big Data Appliance offer?

Drag-and-Drop Interface: Enables non-technical users to build reports effortlessly.
ZFS Technology: Provides cost-effective storage solutions.
Spark on YARN Support: Enhances Hadoop performance with faster processing.
Security Roles: Ensures data access alignment with user privileges.
Multi-Language Support: Assists in handling diverse language processing needs.

What benefits should users look for when evaluating Oracle Big Data Appliance?

Centralized Data Management: Avoids the need for additional data warehouses.
Regulatory Reporting Facilitation: Eases compliance processes in sectors like Medicare.
Role-Based Reporting: Empowers end-users to create reports based on specific needs.
Consultation Integrations: Supports seamless data management when coupled with external services.

Oracle Big Data Appliance is predominantly used in healthcare and financial services, where data consolidation and regulatory compliance are crucial. Entities leverage Oracle's storage capabilities to maintain centralized repositories, expediting regulatory reporting and claim management processes, particularly benefiting sectors like Medicare. Consulting services frequently integrate Oracle platforms to enhance data management efficiency, especially for organizations utilizing Oracle applications.

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

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