Try our new research platform with insights from 80,000+ expert users

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
6.8
Azure Data Factory offers cost-effective, efficient data consolidation for actionable insights, saving time and resources compared to manual processes.
Sentiment score
7.4
Organizations experienced improved performance and cost savings after adopting BigQuery, achieving a 75% cost reduction and efficient data management.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
 

Customer Service

Sentiment score
6.4
Azure Data Factory support is generally satisfactory, with responsive assistance, though some users report delays or costly consulting.
Sentiment score
7.2
Customers generally find BigQuery support helpful, but integration challenges and resource availability need improvement despite positive responsiveness.
The technical support from Microsoft is rated an eight out of ten.
The technical support is responsive and helpful
The technical support for Azure Data Factory is generally acceptable.
rating the customer support at ten points out of ten
I have been self-taught and I have been able to handle all my problems alone.
 

Scalability Issues

Sentiment score
7.5
Azure Data Factory is highly scalable and flexible but has room for improvement with third-party integrations and large datasets.
Sentiment score
8.0
BigQuery offers impressive scalability and efficiency for large data, but may be costly and present integration challenges for smaller users.
Azure Data Factory is highly scalable.
It is a 10 out of 10 in terms of scalability.
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.
 

Stability Issues

Sentiment score
7.8
Azure Data Factory is stable and reliable, with occasional issues in responsiveness and large dataset handling.
Sentiment score
8.5
BigQuery is highly stable and reliable for cloud data analytics, efficiently handling large volumes with minor issues.
The solution has a high level of stability, roughly a nine out of ten.
 

Room For Improvement

Azure Data Factory needs better integration, scheduling, support, AI features, and user interface improvements for efficient data management.
BigQuery users face challenges with migration, integration, cost, scaling, user interfaces, and call for better machine learning capabilities.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
There is a problem with the integration with third-party solutions, particularly with SAP.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
 

Setup Cost

Azure Data Factory offers competitive, flexible pricing based on usage, with costs integrating Azure services and varying significantly.
BigQuery offers flexible, pay-as-you-go pricing based on data usage, with low storage costs and adaptable enterprise plans.
The pricing is cost-effective.
It is considered cost-effective.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
 

Valuable Features

Azure Data Factory excels in data integration with user-friendly features, scalability, and over 100 connectors for seamless data movement.
BigQuery excels in scalability, performance, cost-efficiency, and integration with Google products, making it ideal for complex data analyses.
It connects to different sources out-of-the-box, making integration much easier.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
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.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
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.
 

Categories and Ranking

Azure Data Factory
Ranking in Cloud Data Warehouse
2nd
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
Data Integration (1st)
BigQuery
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
41
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of September 2025, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 6.9%, down from 9.5% compared to the previous year. The mindshare of BigQuery is 7.7%, down from 7.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
Azure Data Factory6.9%
BigQuery7.7%
Other85.4%
Cloud Data Warehouse
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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
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.
report
Use our free recommendation engine to learn which Cloud Data Warehouse solutions are best for your needs.
867,370 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
12%
Manufacturing Company
9%
Government
7%
Computer Software Company
16%
Financial Services Firm
15%
Manufacturing Company
11%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
By reviewers
Company SizeCount
Small Business11
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?
I have not used BigQuery for AI and machine learning projects myself. I know how to use it, and I can see where it would be useful, but so far, in my projects, I have not included a BigQuery compon...
 

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
Information Not Available
Find out what your peers are saying about Azure Data Factory vs. BigQuery and other solutions. Updated: July 2025.
867,370 professionals have used our research since 2012.