Azure Data Factory vs StreamSets comparison

Why PeerSpot?

Microsoft and IBM are both solutions in the Data Integration category. Microsoft is ranked #5 with an average rating of 8.0, while IBM is ranked #24 with an average rating of 9.5. Microsoft holds a 2.2% mindshare in DI, compared to IBM’s 1.1% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Azure Data Factory

Read 97 Azure Data Factory reviews

14,793 Views
10,207 Comparison Views

92% willing to recommend

StreamSets

Read 21 StreamSets reviews

4,501 Views
3,466 Comparison Views

100% willing to recommend

Azure Data Factory

StreamSets

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

Azure Data Factory and StreamSets compete in the data integration and ETL tools category. Azure Data Factory has the upper hand due to its integration within the Microsoft ecosystem and extensive built-in connectors.

Features: StreamSets offers no-code ETL capabilities, ideal for non-technical users, supports batch and streaming with data drift resilience and Change Data Capture. Azure Data Factory provides robust data transformation and integration with over 100 built-in connectors, favoring users with ETL process knowledge.

Room for Improvement: StreamSets faces integration challenges outside Java-based platforms and has a steep learning curve. Users raise concerns about logging and memory management. Azure Data Factory's complex pricing, limited streaming capabilities, and non-Microsoft platform integration require improvement. Documentation clarity and non-native system support are frequently mentioned.

Ease of Deployment and Customer Service: StreamSets is noted for excellent customer support but has a higher initial deployment effort due to its learning curve. It operates in public and hybrid cloud environments. Azure Data Factory leverages Microsoft's cloud infrastructure, easing deployment but complicating Azure resource management, often relying on community support.

Pricing and ROI: StreamSets offers diverse pricing from open source to enterprise, with higher costs for smaller businesses but notable ROI by reducing data drift issues. Azure Data Factory's pay-as-you-go model poses estimation challenges and can become costly with extensive use. Competitive pricing is noted, emphasizing careful management to prevent unexpected expenses.

To learn more, read our detailed Azure Data Factory vs. StreamSets Report (Updated: September 2026).

Buyer's Guide

Azure Data Factory vs. StreamSets

September 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:

ROI

Sentiment score

5.3

Azure Data Factory centralizes data, cuts costs, boosts efficiency, enhances client satisfaction, and delivers a 20-30% return.

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.

Deena Thayalan

Data Engineer at Vthinktechnologies

For more quotes and insights, download the Azure Data Factory report

No quotes available

For more quotes and insights, download the StreamSets report

Customer Service

Sentiment score

6.2

Azure Data Factory support is generally proficient and accessible, though response times and costs can vary for dedicated support.

Sentiment score

6.7

StreamSets support is responsive and knowledgeable, offering effective solutions, though response times and technical handling could improve.

On a scale of one to ten, I would rate the technical support as nine.

Hafiz Abdul Mannan

Senior Consultant Oracle Technologies at a tech vendor with 10,001+ employees

The technical support from Microsoft is rated an eight out of ten.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

The technical support is responsive and helpful

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

For more quotes and insights, download the Azure Data Factory report

IBM technical support sometimes transfers tickets between different teams due to shift changes, which can be frustrating.

SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

For more quotes and insights, download the StreamSets report

Scalability Issues

Sentiment score

7.3

Azure Data Factory is scalable and flexible but sometimes requires support for quotas and improvements for broader scalability.

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.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

I did not experience scalability issues.

SubrataDey

Principal Data Engineer at Oracle

For more quotes and insights, download the Azure Data Factory report

No quotes available

For more quotes and insights, download the StreamSets report

Stability Issues

Sentiment score

7.8

Azure Data Factory is highly rated for stability, though performance varies with setup, data volume, and resource allocation.

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.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

It gives me the accurate result.

Kunal Das

Test Engineer at Happiest Minds Technologies

I have been using Azure Data Factory for a very long time, and I did not find too many issues.

SubrataDey

Principal Data Engineer at Oracle

For more quotes and insights, download the Azure Data Factory report

No quotes available

For more quotes and insights, download the StreamSets report

Room For Improvement

Azure Data Factory needs better setup, connectivity, documentation, support, performance, and UI for enhanced functionality and user experience.

StreamSets struggles with integration, real-time processing, clarity in UI, memory issues, security, documentation, and cloud storage performance.

The ability to handle the largest volumes of data is another concern; if I have to manage more than one terabyte of data every day, I am not comfortable dealing with Azure Data Factory and had to switch to Oracle Data Integrators (ODI) because it lacks performance features.

Hafiz Abdul Mannan

Senior Consultant Oracle Technologies at a tech vendor with 10,001+ employees

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.

Kunal Das

Test Engineer at Happiest Minds Technologies

Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

For more quotes and insights, download the Azure Data Factory report

It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades.

SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

For more quotes and insights, download the StreamSets report

Setup Cost

Azure Data Factory's pricing is competitive but complex, requiring careful monitoring to manage costs for high data usage.

StreamSets provides flexible pricing models, with varied user satisfaction, favoring larger enterprises over smaller companies due to cost.

The pricing is cost-effective.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

It is considered cost-effective.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

For more quotes and insights, download the Azure Data Factory report

No quotes available

For more quotes and insights, download the StreamSets report

Valuable Features

Azure Data Factory excels in performance, ease of use, scalability, and integration, making it highly valued for ETL processes.

StreamSets offers intuitive interface, extensive connectors, and features accessible to non-technical users for seamless data integration and manipulation.

It connects to different sources out-of-the-box, making integration much easier.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.

Deena Thayalan

Data Engineer at Vthinktechnologies

Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

For more quotes and insights, download the Azure Data Factory report

It allows a hybrid installation approach, rather than being completely cloud-based or on-premises.

SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

For more quotes and insights, download the StreamSets report

Categories and Ranking

Azure Data Factory

Ranking in Data Integration

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

StreamSets

Ranking in Data Integration

24th

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 5.5% compared to the previous year. The mindshare of StreamSets is 1.1%, down from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.2%
StreamSets	1.1%
Other	96.7%

Data Integration

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.

Read full review

SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

Enables effective batch loading with visual interface and enterprise support

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 infrastructure. I had to switch to a new EC2 box, even though the processor was not fully utilized. It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades. Additionally, it would be a great enhancement if StreamSets could produce a lineage graph to visualize how the data has passed through the system.

Read full review

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction Company

Financial Services Firm

11%

Outsourcing Company

Comms Service Provider

Manufacturing 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	9
Midsize Enterprise	2
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 needs improvement with StreamSets?

See all answers

What is your primary use case for StreamSets?

We are using StreamSets for batch loading.

See all answers

What advice do you have for others considering StreamSets?

If asked, I definitely recommend StreamSets to other users. My overall rating for the solution is nine.

See all answers

Comparisons

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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 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

SnapLogic vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Pentaho Data Integration and Analytics vs StreamSets

Compared 9% of the time

Informatica PowerCenter vs StreamSets

Compared 6% of the time

SSIS vs StreamSets

Compared 5% of the time

Spring Cloud Data Flow vs StreamSets

Compared 5% of the time

Confluent vs StreamSets

Compared 4% of the time

More StreamSets Competitors

Product Reports

Buyer's Guide

Azure Data Factory

September 2026

Download Azure Data Factory product report

Buyer's Guide

StreamSets

August 2026

Download StreamSets 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

StreamSets streamlines data pipeline creation, connecting data from multiple sources to destinations like cloud platforms with minimal coding. Its centralized platform and intuitive design enhance ETL and data migration processes.

StreamSets integrates seamlessly with analytics platforms, offering tools such as Data Collector and Control Hub to facilitate data ingestion, transformation, and machine learning integrations. Its user-friendly interface and ready connectors aid in configuring complex data pipelines. With built-in data drift resilience and scheduling options, users experience efficient, scalable data management, despite challenges like latency in cloud storage and interface enhancement needs. Users often employ StreamSets for batch loading, real-time data processing, and smart data pipeline management, offering comprehensive data integration solutions.

What are the key features of StreamSets?

Data Collector: Enables streamlined data collection and processing.
Control Hub: Centralizes management of data pipelines.
Minimal Coding: Simplifies pipeline configuration with limited code requirement.
Data Drift Resilience: Adapts to changes in data patterns effectively.

What benefits should users look for?

Time-Saving: Efficient processes reduce manual effort.
Scalability: Easily manages large datasets.
Integration: Connects with leading analytics and cloud platforms.
Real-Time Processing: Supports continuous data delivery for timely insights.

In industries like finance and technology, StreamSets supports data migration, machine learning integrations, and analytics by simplifying data transformation and enhancing decision-making capabilities through its robust pipeline management.

IBM

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

Buyer's Guide

Azure Data Factory vs. StreamSets

September 2026

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