Azure Data Factory vs Dagster Labs comparison

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Azure Data Factory

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Executive Summary

We performed a comparison between Azure Data Factory and Dagster Labs based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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

Azure Data Factory vs. Dagster Labs

September 2026

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Helped 915,341 peers since 2012

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

6th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (6th)

Dagster Labs

Ranking in Data Integration

37th

Average Rating

7.8

Reviews Sentiment

4.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 5.1% compared to the previous year. The mindshare of Dagster Labs is 0.1%. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.2%
Dagster Labs	0.1%
Other	97.7%

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.

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Alekh Shrivastava

IT Consultant at a outsourcing company with 10,001+ employees

Automation and lineage visibility have transformed how our teams schedule and monitor ETL workflows

There are multiple products from Dagster Labs: Dagster Cloud, Dagster Labs, and sometimes it is quite confusing to choose which one for what purposes. I know there are some licensing buckets for small organizations, medium organizations, and bigger organizations, so the modeling and the cost estimation part could be more intuitive, making it easier for beginners to understand how much time and money they would save by onboarding Dagster Labs in their project. I deducted two points because I faced some challenges working with the Git repository integration with Dagster Labs due to some security keys or some weird issue. I had to get in a call with Dagster Labs subject matter expert, and we really struggled for two to three weeks because we could not establish the secure connection through that.

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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

"The most valuable feature I have found at Azure Data Factory is the data flow function."

"I am one hundred percent happy with the stability."

"An excellent tool for pipeline orchestration."

"The most valuable feature of this solution would be ease of use."

"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

"My only advice is that Azure Data Factory, particularly for data ingestion, is a good choice."

"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."

More Azure Data Factory pros

"Since automating with Dagster Labs, I save around 90% of manual effort."

"As a product, it is one of the best I have had the pleasure to work with."

"Dagster Labs has positively impacted my organization because whenever there is a change in the data, it becomes easy for me to schedule the job, run the changes, and make changes to be imported to the slowly changing dimension tables."

"Dagster Labs has positively impacted our organization by making it much easier to create data pipelines, helping us create new use cases, and allowing us to transform all of the data we have into features, which Dagster Labs has greatly assisted with."

"Dagster Labs has impacted my organization positively by enabling faster project delivery since it has reduced a lot of manual efforts, especially the manual scheduling part."

"Dagster Labs has provided a good sense of the data pipeline I have been using, and I have started to trust it even more because I have full confidence in whether it has been updated correctly and if it has failed."

Cons

"The number of standard adaptors could be extended further."

"There's space for improvement in the development process of the data pipelines."

"We require Azure Data Factory to be able to connect to Google Analytics."

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."

"We have experienced some issues with the integration. This is an area that needs improvement."

"But, I feel that if the usage extends beyond a certain threshold, it will start getting expensive."

"To my mind, the solution needs to be more connectable to its own services."

"Data Factory's performance during heavy data processing isn't great."

More Azure Data Factory cons

"I deducted two points because I faced some challenges working with the Git repository integration with Dagster Labs due to some security keys or some weird issue."

"Dagster Labs is currently providing a Dagster University course, but those offerings are somewhat high-level."

"There are many ways Dagster Labs can be improved. I believe the UI is very slow and it prevents loading state."

"Additionally, the billing for Dagster Labs Cloud Plus is somewhat restrictive regarding the number of seats, which often pushes users towards the enterprise plan."

"One major issue I see with Dagster Labs is with retrying the pipeline; of course, there is a retry of the pipeline available in case it fails, but it starts from the beginning at the first step."

Pricing and Cost Advice

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"The cost is based on the amount of data sets that we are ingesting."

"ADF is cheaper compared to AWS."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"Pricing appears to be reasonable in my opinion."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"The solution's pricing is competitive."

"It's not particularly expensive."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction Company

25%

Outsourcing Company

11%

Comms Service Provider

10%

Financial Services Firm

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	5

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 needs improvement with Dagster Labs?

Dagster Labs is currently providing a Dagster University course, but those offerings are somewhat high-level. When a user comes in and tries to learn Dagster Labs, there need to be different kinds ...

What is your primary use case for Dagster Labs?

The main use case for using Dagster Labs is to utilize ETL processes such as extract, transform, and load because we already have raw data, and we perform all the necessary transformations, includi...

What advice do you have for others considering Dagster Labs?

I would advise others looking into using Dagster Labs to utilize all of the features provided by Dagster Labs, as it has many great offerings. I would rate this product a seven out of ten.

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Product Reports

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Azure Data Factory

October 2026

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October 2026

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

Dagster Labs empowers data-centric enterprises to build, manage, and orchestrate complex data pipelines with ease. Its flexible architecture supports efficient data processing, enhancing productivity.

Dagster Labs provides a robust platform designed for performance and scalability, catering to developers and data engineers seeking to streamline data workflow management. It integrates seamlessly with modern data tools, facilitating efficient data pipeline execution and monitoring. Users benefit from its modular approach, enabling customization according to specific project requirements.

What are the key features of Dagster Labs?

Composable Workflows: Allows users to build and manage modular data pipelines with ease.
Dynamic Monitoring: Provides real-time insights into pipeline performance and health.
Integration Support: Seamlessly connects with popular tools like Apache Spark and Pandas.
Scalable Architecture: Ensures efficient handling of data processing workloads.
Flexible Design: Enables tailored solutions for specific data engineering needs.

What benefits and ROI should users look for in reviews?

Improved Efficiency: Users experience increased productivity with streamlined workflows.
Cost Savings: Optimized data pipelines reduce operational costs.
Enhanced Collaboration: Facilitates teamwork through shared workflows and tools.
Increased Agility: Quick adaptation to changing data requirements.
Reliable Performance: Consistent and dependable data processing capabilities.

Dagster Labs is particularly beneficial in industries such as finance, healthcare, and e-commerce, where data-driven insights are crucial. It enables financial institutions to process transactions swiftly, aids healthcare organizations in managing patient data securely, and helps e-commerce platforms optimize customer engagement through data analysis.

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

Information Not Available