Azure Data Factory vs dbt comparison

Microsoft and dbt Labs are both solutions in the Data Integration category. Microsoft is ranked #3 with an average rating of 8.2, while dbt Labs is ranked #17 with an average rating of 7.8. Microsoft holds a 2.8% mindshare in DI, compared to dbt Labs’s 1.7% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of dbt Labs users who would recommend it.

Azure Data Factory

Read 94 Azure Data Factory reviews

10,752 Views
7,526 Comparison Views

92% willing to recommend

dbt

Read 7 dbt reviews

2,056 Views
1,912 Comparison Views

100% willing to recommend

Azure Data Factory

dbt

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Executive SummaryUpdated on Dec 19, 2024

Azure Data Factory and dbt compete in data integration and transformation. ADF often has the advantage due to its extensive integration capabilities, while dbt leads with its strong transformation features.

Features: Azure Data Factory is recognized for its broad connectivity options to various data sources, visually intuitive data flow abilities, and end-to-end data pipeline management. dbt is noted for its focus on transforming data using SQL, providing automated data lineage for better versioning, and emphasizing analytics engineering with data modeling and testing functions.

Ease of Deployment and Customer Service: Azure Data Factory integrates easily into Microsoft ecosystems, benefiting from direct Microsoft support for quick deployment. dbt requires a more technical setup but offers flexibility with cloud-native capabilities and strong open-source community backing. ADF benefits from official support channels, whereas dbt utilizes community-driven resources.

Pricing and ROI: Azure Data Factory offers a pay-as-you-go pricing model, providing business predictability for scaling operations and highlighting its strong ROI aspects with straightforward pricing and support. dbt offers a combination of free open-source tools and a paid SaaS tier, which some find economical, especially when leveraging its transformation capabilities for enhanced data analytics precision, offering compelling ROI.

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

Buyer's Guide

Azure Data Factory vs. dbt

March 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:

Categories and Ranking

Azure Data Factory

Ranking in Data Integration

3rd

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (2nd)

dbt

Ranking in Data Integration

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Quality (6th)

Mindshare comparison

As of March 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.8%, down from 9.7% compared to the previous year. The mindshare of dbt is 1.7%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.8%
dbt	1.7%
Other	95.5%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

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

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.

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Ahmed Shaaban

Principal Data Engineer at Integrant, Inc.

Data teams have streamlined code-driven pipelines and now collaborate faster on shared models

We are still experimenting with testing, but not that much. We are not using some features yet. We are trying to introduce them because we are coming from a background of SSIS. The team used to work with SSIS, Microsoft SQL Server Integration Services. We are still adapting one feature at a time. Currently, we are working with the SQL modules and with the Jinja templating. We are experimenting with testing, but I would say towards the end of this year, we are planning to explore more of the documentation and the data lineage options as well. I would say the benefits are coming from GUI-based tools like SSIS. We have more control on the codebase. We can create something of a system where we can use macros and templating, speeding up the development cycle. We are now trying to introduce a little testing, and also we are using some sort of a CI/CD cycle, so continuous integration and continuous deployment. I do not believe that these kinds of features are that common as a package as a whole package. dbt excels in that area. I used to have a couple of notes about the performance, but lately I have discovered something called dbt Fusion, which, according to dbt Labs, they proclaim is much faster during the parsing of dbt models. However, I would love to see even more of an out-of-the-box solution regarding the testing. They are treating the testing in a good way so far, but I would love to see even more improvement because the whole data testing field is not very mature. It is not the same as software testing; for example, you have test suites, test tools, and profilers, but for data testing, it is not yet that advanced. I would love for dbt to take the lead on 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 of this solution would be ease of use."

"The user interface is very good. It makes me feel very comfortable when I am using the tool."

"The flexibility that Azure Data Factory offers is great."

"Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness."

"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."

"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."

"I can do everything I want with SSIS and Azure Data Factory."

"The most important feature is that it can help you do the multi-threading concepts."

More Azure Data Factory pros

"The product is developer-friendly."

"There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors."

"dbt has positively impacted my organization by allowing us to create our data pipelines much faster, going from ingestion of data to creating a data product in weeks instead of months, and we can do it in-house with the skillset we already have."

"Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh, and the client is now happy because our downtime was drastically reduced when we perform a complete refresh of the data."

"From a developer point of view, I find the ease of development and the code to be the most useful capabilities of dbt."

"I would say the best feature or the most desirable feature for dbt is the ability to write everything in code."

Cons

"Lacks in-built streaming data processing."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."

"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."

"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 product integration with advanced coding options could cater to users needing more customization."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."

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

More Azure Data Factory cons

"dbt can be improved as I find the co-pilot in dbt is not very good, and my team has tried using it but opted to move off it and use other co-pilots such as GitHub."

"If I needed to name a few areas for improvement, I would mention the migration of code to Git and GitHub, which sometimes fails and can be confusing for developers during handover."

"The solution must add more Python-based implementations."

"Dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section as it is not stable."

"Since dbt has a license cost, if a company is small and does not have much budget, they can explore other tools because there are other tools that provide the same functionality at a lower cost."

"Every upgrade is a little bit of a risk for us because we do not know if the workarounds that we developed will be available for the next version."

Pricing and Cost Advice

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

"The price you pay is determined by how much you use it."

"Product is priced at the market standard."

"The licensing cost is included in the Synapse."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

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

"The solution is cheap."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

More Azure Data Factory pricing and cost advice

"The solution’s pricing is affordable."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

11%

Manufacturing Company

Government

Financial Services Firm

13%

Insurance Company

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	20
Large Enterprise	57

By reviewers
Company Size	Count
Small Business	1
Midsize Enterprise	3
Large Enterprise	3

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 is your experience regarding pricing and costs for dbt?

The course content that dbt provides is free and excellent for anyone starting out.

See all answers

What needs improvement with dbt?

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What is your primary use case for dbt?

I am working with one of our enterprise customers, managing their newly established cloud warehouse. They are using Snowflake and we are using dbt to manage all the transformation and views and tab...

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Comparisons

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 5% of the time

Snowflake vs Azure Data Factory

Compared 5% of the time

FME vs Azure Data Factory

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

SSIS vs dbt

Compared 15% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs dbt

Compared 7% of the time

Oracle Data Integrator (ODI) vs dbt

Compared 7% of the time

Informatica PowerCenter vs dbt

Compared 5% of the time

Rivery vs dbt

Compared 3% of the time

More dbt Competitors

Product Reports

Buyer's Guide

Azure Data Factory

March 2026

Download Azure Data Factory product report

Buyer's Guide

dbt

March 2026

Download dbt 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

dbt is a transformational tool that empowers data teams to quickly build trusted data models, providing a shared language for analysts and engineering teams. Its flexibility and robust feature set make it a popular choice for modern data teams seeking efficiency.

Designed to integrate seamlessly with the data warehouse, dbt enables analytics engineers to transform raw data into reliable datasets for analysis. Its SQL-centric approach reduces the learning curve for users familiar with it, allowing powerful transformations and data modeling without needing a custom backend. While widely beneficial, dbt could improve in areas like version management and support for complex transformations out of the box.

What are the most valuable features of dbt?

SQL-Based Development: Facilitates transformations using SQL, streamlining the data modeling process.
Modularity: Encourages reusable components, making projects easier to manage and scale.
Version Control: Integrates with version control systems to track changes and collaborate effectively.
Testing Framework: Ensures data quality through automated tests, increasing trust in data outputs.

What benefits should you expect from using dbt?

Efficiency: Cuts down data processing times, allowing faster access to insights.
Collaboration: Promotes teamwork through shared data models and a common working language.
Scalability: Supports growing data demands by transforming complex data more flexibly and modularly.
Transparency: Provides clear documentation and lineage tracking, aiding in troubleshooting and understanding.

In the finance industry, dbt helps in cleansing and preparing transactional data for analysis, leading to more accurate financial reporting. In e-commerce, it empowers teams to rapidly integrate and analyze customer behavior data, optimizing marketing strategies and improving user experience.

dbt Labs

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

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Buyer's Guide

Azure Data Factory vs. dbt

March 2026

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