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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
7.1
Azure Data Factory offers significant time, effort, and infrastructure savings, enhancing data analysis and decision-making capabilities.
Sentiment score
8.6
Organizations saved costs and improved performance with BigQuery, achieving significant returns despite an initial learning period.
 

Customer Service

Sentiment score
6.5
Azure Data Factory support is praised for responsiveness, though some report delays; satisfaction varies with Microsoft partnerships.
Sentiment score
7.1
Google BigQuery support is generally reliable and agile but lacks direct engagement compared to competitors like Teradata.
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
 

Scalability Issues

Sentiment score
7.5
Azure Data Factory scales efficiently, managing large datasets for enterprises, though users note cost and integration limitations.
Sentiment score
7.9
BigQuery excels in scalability and performance for large operations but may be costly for smaller businesses.
Azure Data Factory is highly scalable.
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 highly rated for stability, scalability, and performance, despite occasional minor issues with larger data volumes.
Sentiment score
8.5
BigQuery is praised for stability, reliability, and performance but has minor glitches with room for improvement in some areas.
The solution has a high level of stability, roughly a nine out of ten.
 

Room For Improvement

Azure Data Factory requires improvements in integration, pricing, documentation, UI, monitoring, processing, and debugging for enhanced user experience.
BigQuery's drawbacks include special character restrictions, high pricing, integration issues, and needed improvements in user interface and support.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
There is a problem with the integration with third-party solutions, particularly with SAP.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
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.
 

Setup Cost

Azure Data Factory offers competitive, flexible pay-as-you-go pricing; costs vary by data volume and use of additional services.
BigQuery's pricing is flexible, based on usage, with low storage costs, and customizable to enterprise needs within Google Cloud.
The pricing is cost-effective.
It is considered cost-effective.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
 

Valuable Features

Azure Data Factory enables easy data integration, management, and transformation with over 100 connectors, supporting ETL and automation efficiently.
BigQuery provides scalable, fast, cost-effective data analytics with seamless GCP integration and supports complex queries and various data types.
It connects to different sources out-of-the-box, making integration much easier.
I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets.
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.
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.
 

Categories and Ranking

Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
90
Ranking in other categories
Data Integration (1st)
BigQuery
Ranking in Cloud Data Warehouse
4th
Average Rating
8.2
Reviews Sentiment
7.3
Number of Reviews
40
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2025, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 9.1%, down from 10.2% compared to the previous year. The mindshare of BigQuery is 7.4%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.
VikashKumar1 - PeerSpot reviewer
Easy to maintain and provides high availability
Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process. Sometimes, we face some issues, bugs, and defects. We must first connect with a VPN to check data issues while working from home. Then, it allows you to connect to the cloud. After logging into the cloud, it searches for the service we are looking for, and then we go to BigQuery. This is a long process. After that, we analyze the issues in a table. Instead, it would be very helpful if it could provide a tool that we can install on our MacBook or Windows system. Once we open this tool, we can connect directly to the BigQuery server and easily perform tasks.
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Computer Software Company
17%
Financial Services Firm
15%
Manufacturing Company
12%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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?
The price is perceived as expensive, rated at eight out of ten in terms of costliness. Still, it offers significant cost savings.
What needs improvement with BigQuery?
When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming. In general, if I know SQL and start playing around, it will star...
 

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: January 2025.
837,501 professionals have used our research since 2012.