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 #18 with an average rating of 7.8. Microsoft holds a 3.0% 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 93 Azure Data Factory reviews

11,065 Views
7,745 Comparison Views

92% willing to recommend

dbt

Read 5 dbt reviews

1,881 Views
1,768 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: February 2026).

Buyer's Guide

Azure Data Factory vs. dbt

February 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

18th

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Data Quality (8th)

Mindshare comparison

As of February 2026, in the Data Integration category, the mindshare of Azure Data Factory is 3.0%, down from 9.8% compared to the previous year. The mindshare of dbt is 1.7%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Azure Data Factory	3.0%
dbt	1.7%
Other	95.3%

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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reviewer2780388

Senior Data Engineer at a pharma/biotech company with 10,001+ employees

Streamlined Data engineering and built-in lineages

The best features of dbt include lineage and Jinja templating languages that make it easy for creating pipelines. The built-in lineage feature provides a good understanding of the several layers where data is being loaded in dbt, allowing visibility from different layers into the end product. dbt has positively impacted version controlling as it has different version control steps involved. The specific improvements seen with version control in dbt are that it has helped trace the data lineage, enabled faster trace and rollbacks, and enabled safe collaboration at every scale, which has improved data quality. A return on investment has been seen from using dbt as the time has reduced while utilizing dbt in the form of data pipelines and ETL scripting. 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.

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

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"We have been using drivers to connect to various data sets and consume data."

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

"Its integrability with the rest of the activities on Azure is most valuable."

"The scalability of the product is impressive."

"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."

"The data flows were beneficial, allowing us to perform multiple transformations."

More Azure Data Factory pros

"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."

"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."

"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."

Cons

"Lacks a decent UI that would give us a view of the kinds of requests that come in."

"The speed and performance need to be improved."

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

"There is no built-in pipeline exit activity when encountering an error."

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

"The pricing scheme is very complex and difficult to understand."

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

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

More Azure Data Factory cons

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

"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."

"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."

"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."

Pricing and Cost Advice

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

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

"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."

"Pricing appears to be reasonable in my opinion."

"Pricing is comparable, it's somewhere in the middle."

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

"The licensing cost is included in the Synapse."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

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

13%

Computer Software Company

11%

Manufacturing Company

Government

Financial Services Firm

14%

Insurance Company

Computer Software Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	19
Large Enterprise	57

No data available

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...

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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...

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What is your experience regarding pricing and costs for dbt?

The pricing, setup cost, and licensing cost are managed by our infrastructure teams. As data engineers, we are not familiar with these details. I need to check with my infrastructure team on whethe...

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What needs improvement with dbt?

I am not very familiar with dbt's version control system. I cannot identify any improvements in dbt because I am still exploring more functionality. I have been working with dbt for only three year...

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What is your primary use case for dbt?

I am currently working with dbt and Snowflake together. We use dbt for data transformation purposes. We obtain the data and store the raw data directly into Snowflake, then perform all transformati...

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Comparisons

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

Compared 6% of the time

Snowflake vs Azure Data Factory

Compared 6% of the time

FME vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 4% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

SSIS vs dbt

Compared 19% of the time

Oracle Data Integrator (ODI) vs dbt

Compared 9% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs dbt

Compared 7% of the time

Informatica PowerCenter vs dbt

Compared 7% of the time

WhereScape RED vs dbt

Compared 4% of the time

More dbt Competitors

Product Reports

Buyer's Guide

Azure Data Factory

February 2026

Download Azure Data Factory product report

Buyer's Guide

dbt

February 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

February 2026

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