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Azure Data Factory vs StreamSets comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024

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
6.8
Azure Data Factory offers cost-effective, efficient data consolidation for actionable insights, saving time and resources compared to manual processes.
Sentiment score
8.1
StreamSets speeds up data processing, boosts efficiency and revenue, simplifies tasks, enhances security, and reduces costs significantly.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
 

Customer Service

Sentiment score
6.4
Azure Data Factory support is generally satisfactory, with responsive assistance, though some users report delays or costly consulting.
Sentiment score
6.7
StreamSets support is responsive and knowledgeable, offering effective solutions, though response times and technical handling could improve.
The technical support is responsive and helpful
The technical support from Microsoft is rated an eight out of ten.
The technical support for Azure Data Factory is generally acceptable.
IBM technical support sometimes transfers tickets between different teams due to shift changes, which can be frustrating.
 

Scalability Issues

Sentiment score
7.5
Azure Data Factory is highly scalable and flexible but has room for improvement with third-party integrations and large datasets.
Sentiment score
7.6
StreamSets is scalable and flexible, favored for cloud use but could improve auto-scaling for large data migrations.
Azure Data Factory is highly scalable.
 

Stability Issues

Sentiment score
7.8
Azure Data Factory is stable and reliable, with occasional issues in responsiveness and large dataset handling.
Sentiment score
7.8
StreamSets is praised for stability and reliability, despite minor memory issues, with high user ratings and market competitiveness.
The solution has a high level of stability, roughly a nine out of ten.
 

Room For Improvement

Azure Data Factory needs better integration, scheduling, support, AI features, and user interface improvements for efficient data management.
StreamSets struggles with integration, real-time processing, clarity in UI, memory issues, security, documentation, and cloud storage performance.
Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
When using Git services, there are challenges with linked services and triggers getting overridden when moving between different environments (Dev, UAT, Prod).
It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades.
 

Setup Cost

Azure Data Factory offers competitive, flexible pricing based on usage, with costs integrating Azure services and varying significantly.
StreamSets provides flexible pricing models, with varied user satisfaction, favoring larger enterprises over smaller companies due to cost.
The pricing is cost-effective.
It is considered cost-effective.
 

Valuable Features

Azure Data Factory excels in data integration with user-friendly features, scalability, and over 100 connectors for seamless data movement.
StreamSets offers intuitive interface, extensive connectors, and features accessible to non-technical users for seamless data integration and manipulation.
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.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
It connects to different sources out-of-the-box, making integration much easier.
It allows a hybrid installation approach, rather than being completely cloud-based or on-premises.
 

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)
StreamSets
Ranking in Data Integration
22nd
Average Rating
8.4
Reviews Sentiment
7.0
Number of Reviews
21
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of September 2025, in the Data Integration category, the mindshare of Azure Data Factory is 5.6%, down from 11.6% compared to the previous year. The mindshare of StreamSets is 1.4%, down from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory5.6%
StreamSets1.4%
Other93.0%
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.
Ved Prakash Yadav - PeerSpot reviewer
Useful for data transformation and helps with column encryption
We use various tools and alerting systems to notify us of pipeline errors or failures. StreamSets supports data governance and compliance by allowing us to encrypt incoming data based on specified rules. We can easily encrypt columns by providing the column name and hash key. If you're considering using StreamSets for the first time, I would advise first understanding why you want to use it and how it will benefit you. If you're dealing with change tracking or handling large amounts of data, it could be cost-effective compared to services like Amazon. It's easy to schedule and manage tasks with the tool, and you can enhance your skills as an ETL developer. You can easily migrate traditional pipelines built on platforms like Informatica or Talend to StreamSets. I rate the overall solution an eight out of ten.
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
12%
Manufacturing Company
9%
Government
6%
Computer Software Company
11%
Manufacturing Company
10%
Insurance Company
9%
Financial Services Firm
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise18
Large Enterprise55
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise2
Large Enterprise11
 

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...
What do you like most about StreamSets?
The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customiz...
What needs improvement with StreamSets?
One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infr...
What is your primary use case for StreamSets?
We are using StreamSets for batch loading.
 

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
Availity, BT Group, Humana, Deluxe, GSK, RingCentral, IBM, Shell, SamTrans, State of Ohio, TalentFulfilled, TechBridge
Find out what your peers are saying about Azure Data Factory vs. StreamSets and other solutions. Updated: July 2025.
866,744 professionals have used our research since 2012.