Azure Data Factory vs BigQuery comparison

Microsoft and Google are both solutions in the Cloud Data Warehouse category. Microsoft is ranked #3 with an average rating of 8.1, while Google is ranked #5 with an average rating of 8.2. Microsoft holds a 12.8% mindshare in CDW, compared to Google’s 9.1% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Azure Data Factory

Read 86 Azure Data Factory reviews

5,558 Views
4,401 Comparison Views

92% willing to recommend

BigQuery

Read 38 BigQuery reviews

4,187 Views
3,336 Comparison Views

100% willing to recommend

Azure Data Factory

BigQuery

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 18, 2024

Azure Data Factory and BigQuery are competitors in the data management and analytics category. Azure Data Factory holds an advantage in data integration and orchestration, while BigQuery excels in complex querying and analytics performance.

Features: Azure Data Factory offers a drag-and-drop interface for simplified data transformations, integration with SAP and databases, and seamless Azure ecosystem connectivity. BigQuery provides advanced SQL querying, high performance for large-scale data analytics, and strong integration with GCP products.

Room for Improvement: Azure Data Factory users seek better machine learning capabilities, improved API handling, and enhanced user interface elements. BigQuery could benefit from increased user-friendliness, simplified query handling, and extended local data residency options.

Ease of Deployment and Customer Service: Azure Data Factory supports hybrid and on-premises deployments, making it versatile for diverse infrastructures, while BigQuery is optimized for public cloud deployment within the Google ecosystem. Both offer comprehensive support; however, Azure's support can sometimes be slow, and BigQuery could improve proactive service.

Pricing and ROI: Azure Data Factory's pay-as-you-go pricing is competitive but complex as data volumes grow, justified by efficiencies gained. BigQuery's pricing is affordable yet flexible, offering cost-effectiveness for significant data workloads and substantial ROI from large-scale analytics.

To learn more, read our detailed Azure Data Factory vs. BigQuery Report (Updated: December 2024).

Buyer's Guide

Azure Data Factory vs. BigQuery

December 2024

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Categories and Ranking

Azure Data Factory

Ranking in Cloud Data Warehouse

3rd

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Integration (1st)

BigQuery

Ranking in Cloud Data Warehouse

5th

Average Rating

8.2

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2024, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 12.8%, down from 13.4% compared to the previous year. The mindshare of BigQuery is 9.1%, up from 7.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

Thulani David Mngadi

Data architect at Old Mutual

Data flow feature is valuable for data transformation tasks

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.

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Sathishkumar Jayaprakash

Engineer at HTC Global Services (INDIA) Private

Efficient large dataset handling with seamless service integration

BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases. It integrates well with other GCP products, and creating subscriptions in the UI is straightforward. The whole ecosystem of GCP products makes BigQuery beneficial for our data-handling tasks. Additionally, it is more cost-effective compared to alternatives like AWS.

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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

"In terms of my personal experience, it works fine."

"It is beneficial that the solution is written with Spark as the back end."

"We have found the bulk load feature very valuable."

"Data Factory's most valuable feature is Copy Activity."

"It makes it easy to collect data from different sources."

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

"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."

"The most valuable feature is the copy activity."

More Azure Data Factory pros

"The most valuable aspect of BigQuery is its ability to handle high data workloads without causing friction with our online systems."

"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."

"Its integration with other tools like Atlan through a Google Chrome extension is highly beneficial."

"The setup is simple."

"The initial setup is straightforward."

"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."

"BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."

"BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases."

More BigQuery pros

Cons

"Data Factory's cost is too high."

"We have experienced some issues with the integration. This is an area that needs improvement."

"The Microsoft documentation is too complicated."

"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."

"The product's technical support has certain shortcomings, making it an area where improvements are required."

"The speed and performance need to be improved."

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."

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

More Azure Data Factory cons

"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."

"BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses."

"They could enhance the platform's user accessibility."

"Sometimes, support specialists might not have enough experience or business understanding, which can be an issue."

"The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

"The solution hinges on Google patterns so continued improvement is important."

"I rate BigQuery six out of 10 for affordability. It could be cheaper."

More BigQuery cons

Pricing and Cost Advice

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

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"The pricing model is based on usage and is not cheap."

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

"This is a cost-effective solution."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"The solution is cheap."

More Azure Data Factory pricing and cost advice

"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."

"The product’s pricing could be more flexible for end users."

"The price is a bit high but the technology is worth it."

"The solution's pricing is cheaper compared to other solutions."

"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."

"The pricing appears to be competitive for the intended usage scenarios we have in mind."

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

"One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables. BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use."

More BigQuery pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Computer Software Company

16%

Financial Services Firm

14%

Manufacturing Company

12%

Retailer

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.

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...

See all answers

What do you like most about BigQuery?

The initial setup process is easy.

See all answers

What is your experience regarding pricing and costs for BigQuery?

We are above the free threshold, so we are paying around 40 euros per month for BigQuery. It is generally low cost.

See all answers

What needs improvement with BigQuery?

Sometimes, support specialists might not have enough experience or business understanding, which can be an issue. They might have basic knowledge but lack specific insights related to the specific ...

See all answers

Comparisons

Informatica PowerCenter vs Azure Data Factory

Compared 13% of the time

Snowflake vs Azure Data Factory

Compared 10% of the time

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

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

Snowflake vs BigQuery

Compared 38% of the time

Teradata vs BigQuery

Compared 15% of the time

Vertica vs BigQuery

Compared 12% of the time

Oracle Autonomous Data Warehouse vs BigQuery

Compared 12% of the time

Apache Hadoop vs BigQuery

Compared 7% of the time

More BigQuery Competitors

Product Reports

Buyer's Guide

Azure Data Factory

December 2024

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Buyer's Guide

BigQuery

December 2024

Download BigQuery product report

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Microsoft

Google

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.

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

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. BigQuery

December 2024

Free Report: Azure Data Factory vs. BigQuery

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