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Azure Data Factory vs BigQuery comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

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
6.1
Azure Data Factory users save time and reduce costs, achieving ROI and enhanced satisfaction with centralized data integration.
Sentiment score
4.9
BigQuery offers improved performance, cost savings, and intuitive features, with some users reporting up to 75% cost reductions.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
Data Engineer at Vthinktechnologies
 

Customer Service

Sentiment score
6.3
Azure Data Factory users praise support and documentation, but note delays and high costs in paid consulting services.
Sentiment score
7.2
Customers find Google BigQuery support effective but sometimes limited, relying on documentation, forums, and expert assistance for issues.
The technical support from Microsoft is rated an eight out of ten.
Chief Analytics Officer at Idiro Analytics
The technical support is responsive and helpful
Sr. Technical Architect at Hexaware Technologies Limited
They are not slow on responding or very informative.
Sales & Projects Manger at ACS
rating the customer support at ten points out of ten
Sr. Team Lead - IT at InfoStretch
I have been self-taught and I have been able to handle all my problems alone.
Chief Technical Lead at a consultancy with 201-500 employees
I would rate their customer service pretty good on a scale of one to 10, as they gave me access to the platform on a grant.
Principal at Sgt Suds
 

Scalability Issues

Sentiment score
7.4
Azure Data Factory is scalable and cloud-native, suitable for medium to large projects, despite some integration limitations.
Sentiment score
7.8
BigQuery offers excellent scalability and efficiency, supporting vast data seamlessly, though cost and integration challenges may arise.
Azure Data Factory is highly scalable.
Chief Analytics Officer at Idiro Analytics
It is a 10 out of 10 in terms of scalability.
Chief Technical Lead at a consultancy with 201-500 employees
We have not seen problems with scaling.
Director at a consultancy with 11-50 employees
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
Expert Analyst at a healthcare company with 5,001-10,000 employees
 

Stability Issues

Sentiment score
7.7
Azure Data Factory is reliable, with minor connection issues and improved stability, despite occasional backward compatibility changes.
Sentiment score
8.3
BigQuery is stable and reliable, with efficient data handling, few bugs, and strong support, despite occasional slow queries.
The solution has a high level of stability, roughly a nine out of ten.
Chief Analytics Officer at Idiro Analytics
In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google.
Director at a consultancy with 11-50 employees
 

Room For Improvement

Azure Data Factory requires better integration, user interface, pricing, real-time processing, connectors, and improved compatibility with Azure services.
BigQuery struggles with cost, accessibility, scalability, and lacks data residency, needing better integration, performance, and machine learning features.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Chief Analytics Officer at Idiro Analytics
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
Sr. Technical Architect at Hexaware Technologies Limited
There is a problem with the integration with third-party solutions, particularly with SAP.
Solution Architect at Mercedes-Benz AG
Troubleshooting requires opening each pipeline individually, which is time-consuming.
Sr. Team Lead - IT at InfoStretch
In general, if I know SQL and start playing around, it will start making sense.
Expert Analyst at a healthcare company with 5,001-10,000 employees
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
Chief Technical Lead at a consultancy with 201-500 employees
 

Setup Cost

Azure Data Factory pricing is complex, varying with data usage and integrations, leading to unpredictable monthly costs.
BigQuery uses a pay-as-you-go model, balancing affordability with strategic expense management for data storage and query execution.
The pricing is cost-effective.
Chief Analytics Officer at Idiro Analytics
It is considered cost-effective.
Sr. Technical Architect at Hexaware Technologies Limited
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
Chief Technical Lead at a consultancy with 201-500 employees
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
Sr. Team Lead - IT at InfoStretch
 

Valuable Features

Azure Data Factory excels with scalability, ease of use, robust data transformations, seamless orchestration, and extensive connector support.
BigQuery provides scalable, serverless data processing with fast query capabilities, seamless GCP integration, SQL support, and competitive pricing.
It connects to different sources out-of-the-box, making integration much easier.
Sr. Technical Architect at Hexaware Technologies Limited
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
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.
Director at a computer software company with 1,001-5,000 employees
It is really fast because it can process millions of rows in just a matter of one or two seconds.
Expert Analyst at a healthcare company with 5,001-10,000 employees
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
Sr. Team Lead - IT at InfoStretch
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data.
Chief Technical Lead at a consultancy with 201-500 employees
 

Categories and Ranking

Azure Data Factory
Ranking in Cloud Data Warehouse
2nd
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
94
Ranking in other categories
Data Integration (3rd)
BigQuery
Ranking in Cloud Data Warehouse
4th
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
43
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 5.4%, down from 8.5% compared to the previous year. The mindshare of BigQuery is 8.0%, up from 7.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
Azure Data Factory5.4%
BigQuery8.0%
Other86.6%
Cloud Data Warehouse
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
Luís Silva - PeerSpot reviewer
Chief Technical Lead at a consultancy with 201-500 employees
Handles large data sets efficiently and offers flexible data management capabilities
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
11%
Manufacturing Company
9%
Government
6%
Financial Services Firm
15%
Manufacturing Company
14%
Computer Software Company
10%
Media Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise57
By reviewers
Company SizeCount
Small Business13
Midsize Enterprise9
Large Enterprise20
 

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 do you like most about BigQuery?
The initial setup process is easy.
What is your experience regarding pricing and costs for BigQuery?
I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so ...
What needs improvement with BigQuery?
With what I have seen in BigQuery, I had some response times problems, but then it is an analytical database and not a transactional database, so it comes with eventual consistency. I cannot have e...
 

Overview

 

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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Find out what your peers are saying about Azure Data Factory vs. BigQuery and other solutions. Updated: March 2026.
884,797 professionals have used our research since 2012.