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: July 2026).

Azure Data Factory vs. Dagster Labs

July 2026

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Helped 909,725 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

5th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th)

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 August 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.3%, down from 7.2% compared to the previous year. The mindshare of Dagster Labs is 0.0%. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.3%
Dagster Labs	0.0%
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 scalability of the product is impressive."

"It is a complete ETL Solution."

"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."

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

"The most valuable aspect is the copy capability."

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."

"The best part of this product is the extraction, transformation, and load."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

More Azure Data Factory pros

"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 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 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."

"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."

"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."

Cons

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

"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."

"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."

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

"Some prebuilt data source or data connection aspects are generic."

"When we initiated the cluster, it took some time to start the process."

"The product could provide more ways to import and export data."

"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."

More Azure Data Factory cons

"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."

"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."

"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."

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

Pricing and Cost Advice

"Data Factory is affordable."

"The pricing model is based on usage and is not cheap."

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."

"I don't see a cost; it appears to be included in general support."

"I would not say that this product is overly expensive."

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

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

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

28%

Outsourcing Company

Comms Service Provider

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

August 2026

Download Dagster Labs product report

August 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