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Azure Data Factory vs SharePlex comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Aug 19, 2025

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 Data Integration
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
Cloud Data Warehouse (2nd)
SharePlex
Ranking in Data Integration
47th
Average Rating
9.0
Reviews Sentiment
7.3
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the Data Integration category, the mindshare of Azure Data Factory is 5.2%, down from 11.0% compared to the previous year. The mindshare of SharePlex is 0.8%, up from 0.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory5.2%
SharePlex0.8%
Other94.0%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
KW
It reduces the downtime and migration time exponentially
I would rate SharePlex's high availability, and disaster recovery features highly. It works as advertised in terms of rapid fill-over and switch-over opportunities. It reduces the migration time for a multi-terabyte database to an hour or less because it performs real-time replication. SharePlex reduces downtime and migration time exponentially. Something that could take 12 to 24 hours is cut down to one to two hours. By the time you start to migrate, the only remaining replication is a slight difference in data from the point when SharePlex has shut down, and the application has to also switch over to the new database. It allowed the company to replicate data more transparently. Some of the business executives probably don't even know it's there. It works 100 percent of the time, with little downtime or problems. It provides what is needed. There's little concern about whether or not it functions. It's allowed the company to achieve its objectives with its clients, reducing and minimizing downtime for substantial migrations and upgrades. It can reduce hardware and storage costs, but my company doesn't utilize it with high-availability architecture.

Quotes from Members

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

Pros

"The most valuable feature is the ease in which you can create an ETL pipeline."
"The data flows were beneficial, allowing us to perform multiple transformations."
"The flexibility that Azure Data Factory offers is great."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"The solution has a good interface and the integration with GitHub is very useful."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness."
"The core features of the solution we like are the reliability of the data transfer and the accuracy of data read and write. The stability of the solution is also excellent."
"There are some capabilities within SharePlex where you can see how the data is migrating and if it still maintains good data integrity. For example, if there are some tables that get out of sync, there are ways to find them and fix the problem on the spot. Since these are very common issues, we can easily fix these types of problems using utilities, like compare and repair. So, if you find something is out of sync, then you can just repair that table. It basically syncs that table from source to target to see if there are any differences. It will then replicate those differences to the target."
"The core replication and its performance. Performance is crucial, and SharePlex is by far the fastest. The way it handles replication to multiple targets along with basic filtering, as well as from multiple sources to a single target, is very efficient."
"Because of the volume of the transactions, we heavily use a feature that allows SharePlex to replicate thousands of transactions. It's called PEP, Post Enhancement Performance, and that has helped us scale tremendously."
"I like SharePlex's Compare and Repair tool."
 

Cons

"Some prebuilt data source or data connection aspects are generic."
"The setup and configuration process could be simplified."
"Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"The number of standard adaptors could be extended further."
"The solution needs to be more connectable to its own services."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"The reporting features need improvement. It would be very good for users to have a clear understanding of the status of replication."
"For its function in relation to replication (i.e. filtering), I'd give it a six or seven out of 10. GoldenGate has much more functionality by comparison."
"I would like more ability to automate installation and configuration in line with some of the DevOps processes that are more mature in the market. That would be a considerable improvement."
"I would like the solution to have some kind of machine learning and AI capabilities. Often, if we want to improve the performance of posting, we have to bump up a parameter. That means we need to stop the process, come up with a figure that we want to bump the parameter up to, and then start SharePlex. Machine learning and AI capabilities for these kinds of improvement would tremendously help boost productivity for us."
"I don't know how easy it would be to change the architecture in an already implemented replication. For example, if we have a certain way of architecting for a particular database migration and want to change that during a period of time, is that an easy or difficult change? There was a need for us to change the architecture in-between the migration, but we didn't do it. We thought, "This is possibly complicated. Let's not change it in the middle because we were approaching our cutover date." That was one thing that we should have checked with support about for training."
 

Pricing and Cost Advice

"The solution is cheap."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"The pricing model is based on usage and is not cheap."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"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."
"Data Factory is affordable."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"Data Factory is expensive."
"It's really good value for the money. There are some things they could improve on, but in terms of the pricing, features, and support, as a holistic package, we are not thinking of anything else at this point in time."
"It is not as expensive as Oracle GoldenGate and has worked really well within our budgets."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Government
7%
Financial Services Firm
17%
Manufacturing Company
9%
Computer Software Company
8%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
No data available
 

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...
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Also Known As

No data available
Dell SharePlex, SharePlex
 

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
Bodybuilding.com, Priceline.com, Ameco Beijing, Viasat, SK Broadband
Find out what your peers are saying about Azure Data Factory vs. SharePlex and other solutions. Updated: September 2025.
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