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Azure Data Factory vs Equalum [EOL] comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 15, 2026

Review summaries and opinions

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

ROI

Sentiment score
6.1
Azure Data Factory users save time and reduce costs, achieving ROI and enhanced satisfaction with centralized data integration.
Sentiment score
6.9
Equalum EOL accelerates data delivery, reduces development needs, enhances integration speed, and offers cost-saving by streamlining processes.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
Data Engineer at Vthinktechnologies
 

Customer Service

Sentiment score
6.3
Azure Data Factory users praise support and documentation, but note delays and high costs in paid consulting services.
Sentiment score
8.2
Equalum's customer service is praised for responsiveness, technical expertise, multilingual support, and significant upgrades enhancing user experience.
They are not slow on responding or very informative.
Sales & Projects Manger at ACS
The technical support is responsive and helpful
Sr. Technical Architect at Hexaware Technologies Limited
The technical support from Microsoft is rated an eight out of ten.
Chief Analytics Officer at Idiro Analytics
 

Scalability Issues

Sentiment score
7.4
Azure Data Factory is scalable and cloud-native, suitable for medium to large projects, despite some integration limitations.
Sentiment score
7.7
Equalum provides scalable data flow solutions with controllable environments, supporting both vertical and horizontal expansion for growing businesses.
Azure Data Factory is highly scalable.
Chief Analytics Officer at Idiro Analytics
 

Stability Issues

Sentiment score
7.7
Azure Data Factory is reliable, with minor connection issues and improved stability, despite occasional backward compatibility changes.
Sentiment score
7.5
Equalum offers strong stability and performance, with rare network issues, while navigating initial phases presents minor challenges.
The solution has a high level of stability, roughly a nine out of ten.
Chief Analytics Officer at Idiro Analytics
 

Room For Improvement

Azure Data Factory requires better integration, user interface, pricing, real-time processing, connectors, and improved compatibility with Azure services.
Equalum EOL requires improvements in notifications, connectors, documentation, UI, deployment, security, replication, and database integration.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
Sr. Technical Architect at Hexaware Technologies Limited
It's very difficult to come up with even a general understanding of what expenses a client can have with this product.
Data Architect/Engineering manager at Intellias
There is a problem with the integration with third-party solutions, particularly with SAP.
Solution Architect at Mercedes-Benz AG
 

Setup Cost

Azure Data Factory pricing is complex, varying with data usage and integrations, leading to unpredictable monthly costs.
Equalum's pricing is considered reasonable and competitive, focusing on CPU and connector additions, despite perceptions of expensiveness.
The pricing is cost-effective.
Chief Analytics Officer at Idiro Analytics
It is considered cost-effective.
Sr. Technical Architect at Hexaware Technologies Limited
 

Valuable Features

Azure Data Factory excels with scalability, ease of use, robust data transformations, seamless orchestration, and extensive connector support.
Equalum streamlines data flow between SQL Server and Oracle with real-time streaming, no-code interface, and intuitive monitoring features.
The orchestration features in Azure Data Factory are definitely useful, as it is not only for Azure Data Factory; we can also include DataBricks and other services for integrating the data solution, making it a very beneficial feature.
Director at a computer software company with 1,001-5,000 employees
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
Data Engineer at Vthinktechnologies
It connects to different sources out-of-the-box, making integration much easier.
Sr. Technical Architect at Hexaware Technologies Limited
 

Categories and Ranking

Azure Data Factory
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
94
Ranking in other categories
Data Integration (4th), Cloud Data Warehouse (5th)
Equalum [EOL]
Average Rating
9.2
Reviews Sentiment
7.1
Number of Reviews
7
Ranking in other categories
No ranking in other categories
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
reviewer1525674 - PeerSpot reviewer
Managing Director at a consultancy with 11-50 employees
Frees staff to focus on data workflow and on what can be done with data, and away from the details of the technology
There are areas they can do better in, like most software companies that are still relatively young. They need to expand their capabilities in some of the targets, as well as source connectors, and native connectors for a number of large data sources and databases. That's a huge challenge for every company in this area, not just Equalum. If I had the wherewithal to create a tool that could allow for all that connectivity, it would be massive, out-of-the-box. There are all the updates every month. An open source changes constantly, so compatibility for these sources or targets is not easy. And a lot of targets are proprietary and they actually don't want you to connect with them in real time. They want to keep that connectivity for their own competitive tool. What happens is that a customer will say, "Okay, I've got Oracle, and I've got MariaDB, and I've got SQL Server over here, and I've got something else over there. And I want to aggregate that, and put it into Google Cloud Platform." Having connectors to all of those is extremely difficult, as is maintaining them. So there are major challenges to keeping connectivity to those data sources, especially at a CDC level, because you've got to maintain your connectors. And every change that's made with a new version that comes out means they've got to upgrade their version of the connector. It's a real challenge in the industry. But one good thing about Equalum is that they're up for the challenge. If there's a customer opportunity, they will develop and make sure that they update a connector to meet the needs of the customer. They'll also look at custom development of connectors, based on the customer opportunity. It's a work in progress. Everybody in the space is in the same boat. And it's not just ETL tools. It's everybody in the Big Data space. It's a challenge. The other area for improvement, for Equalum, is their documentation of the product. But that comes with being a certain size and having a marketing team of 30 or 40 people and growing as an organization. They're getting there and I believe they know what the deficiencies are. Maintaining and driving a channel business, like Equalum is doing, is really quite a different business model than the direct-sales model. It requires a tremendous amount of documentation, marketing information, and educational information. It's not easy.
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
10%
Manufacturing Company
9%
Government
6%
Comms Service Provider
19%
Retailer
11%
Financial Services Firm
11%
Museum Or Institution
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise57
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise1
Large Enterprise4
 

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.
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...
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...
Why should I use Equalum instead of LogMiner?
You'd want to use the Equalium Oracle Binary Log Parser because it's just better than the LogMiner. Sure, LogMiner is made by Oracle and probably the team knows some insight to make it efficient th...
Is Equalum compatible with all databases?
I'm using Equalum's data replication software for Oracle because that's the one database it's designed for. While it may sound limiting, when you find out how many solutions it can provide for you ...
Can I use Equalum for free?
No, it's not free but you can benefit from a free trial, though. There's an option to try their platform for a limited amount of time, so that may be useful to help you decide if you want to contin...
 

Overview

 

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
SIEMENS, GSK, Wal-Mart, T Systems
Find out what your peers are saying about Informatica, Microsoft, Qlik and others in Data Integration. Updated: May 2026.
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