Azure Data Factory vs Snowflake Analytics comparison

Microsoft and Snowflake Computing are both solutions in the Cloud Data Warehouse category. Microsoft is ranked #3 with an average rating of 8.1, while Snowflake Computing is ranked #7 with an average rating of 8.3. Microsoft holds a 12.8% mindshare in CDW, compared to Snowflake Computing’s 0.7% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Snowflake Computing 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

Snowflake Analytics

Read 37 Snowflake Analytics reviews

390 Views
267 Comparison Views

100% willing to recommend

Azure Data Factory

Snowflake Analytics

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 18, 2024

Azure Data Factory and Snowflake Analytics are leading data integration solutions. Azure Data Factory appears to have an edge in terms of versatile deployment options and integration flexibility. However, Snowflake Analytics stands out for its scalability and ease of management.

Features: Azure Data Factory offers extensive integration options with over 100 connectors and robust ETL capabilities, allowing users to build flexible data flow pipelines. It is noted for its good performance and ease of use with a user-friendly interface. Snowflake Analytics, meanwhile, is recognized for its efficient data analytics, performance, and scalability. Its built-in infrastructure requires minimal management, perfect for organizations prioritizing data-driven decision-making.

Room for Improvement: Azure Data Factory could improve its user interface and enhance integration, especially concerning machine learning and third-party tools. There is also room for more transparent pricing and streamlined integration processes. Snowflake Analytics needs better integration with machine learning tools and a more intuitive user interface. Additional support for transactional processing could enhance its real-time data handling capabilities.

Ease of Deployment and Customer Service: Both Azure Data Factory and Snowflake Analytics support multi-cloud deployment, but Azure Data Factory provides greater flexibility, including hybrid and on-premises options. Azure Data Factory is praised for its support, though response times can be slow. Snowflake Analytics generally receives satisfactory support feedback, but there's room for improvement in providing technical assistance for complex deployments.

Pricing and ROI: Azure Data Factory operates on a pay-as-you-go model, which can be complex due to unpredictable usage-based costs. It’s considered cost-effective, offering good ROI through savings on manual effort and infrastructure. Snowflake Analytics offers competitive pricing models including pay-as-you-go and pre-purchased credits, which some users find expensive. However, the solution's scalability and ease of use justify its pricing. Both solutions contribute positively to ROI, though their distinct pricing approaches influence user perspectives differently.

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

Buyer's Guide

Azure Data Factory vs. Snowflake Analytics

December 2024

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Categories and Ranking

Azure Data Factory

Ranking in Cloud Data Warehouse

3rd

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Integration (1st)

Snowflake Analytics

Ranking in Cloud Data Warehouse

7th

Average Rating

8.4

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Web Analytics (1st)

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 Snowflake Analytics is 0.7%, down from 0.8% 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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Manoj Kambli

Director at Hexaware Technologies Limited

For processing data, it can be easily optimized and supports structured and unstructured data

In terms of cost. Many customers face issues with the expenses on Snowflake. The pricing visibility is complex. If you understand pricing, you can estimate costs, but if not, it can be challenging. They need to provide better cost visibility upfront. For example, credits are not always utilized properly, which isn't easy for new users. A more user-friendly cost calculator would help, as the current one requires expertise to use effectively. These things are not easy for someone who is new to Snowflake, unlike Azure's calculators, which give good visibility into the cost. It does have a calculator, but you need to be skilled in using it. Otherwise, it's hard to estimate the total cost for future loads. For additional features, there could be its own AI model like Google has Gemini. Snowflake can come up with those kinds of features for Analytics. If you can just type in your prompt and get answers, especially for Analytics people on the client side, they will love that. Like, in Google's Gemini, you type the prompt and get the answers based on the data available. If they come up with that kind of AI and UI, it will be easier for clients to analyze their data. They can just type a query like, "What is my highest selling product?" They don't have to write a complex query.

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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

"I like that it's a monolithic data platform. This is why we propose these solutions."

"The data copy template is a valuable feature."

"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."

"The most valuable feature is the copy activity."

"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."

"The most valuable features are data transformations."

"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."

"Powerful but easy-to-use and intuitive."

More Azure Data Factory pros

"The most valuable feature of Snowflake Analytics is the ability to control and manage the cost."

"The most valuable feature of Snowflake Analytics is its performance."

"One of the key advancements in Snowflake Analytics is data sharing."

"Its structure aids in storing, managing, and analyzing large datasets in the cloud, using various relationships like one-to-one and many-to-one, ensuring consistency in the data structure."

"It is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning."

"The Snowflake features I find most beneficial for data analysis are primarily related to analytics, particularly their features like materialized views and queues, which are especially useful for dashboarding purposes."

"The platform not only provides ease of use but also stands out for its speedy execution, conveying a sense of robustness and reliability that I find appealing."

"Very good flexibility and it offers computation completely decoupled from the storage."

More Snowflake Analytics pros

Cons

"The support and the documentation can be improved."

"Azure Data Factory can improve by having support in the drivers for change data capture."

"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."

"It would be better if it had machine learning capabilities."

"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."

"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."

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."

More Azure Data Factory cons

"The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms."

"There are issues while loading data from Snowflake Analytics to the Power BI reporting."

"Integration into different Python and Jupyter notebooks needs to be improved."

"The UI must be improved."

"The solution needs to consider including some updates in the future."

"The solution’s scalability could be improved."

"Snowflake could improve in the areas of advanced machine learning AML and generative AI."

"The UI could be more user-friendly."

More Snowflake Analytics cons

Pricing and Cost Advice

"Product is priced at the market standard."

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

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

"I would rate Data Factory's pricing nine out of ten."

"The price you pay is determined by how much you use it."

"It's not particularly expensive."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

More Azure Data Factory pricing and cost advice

"The cost of Snowflake Analytics is low, any small organization can use it."

"The product's pricing is subjective."

"I have been using free trial version."

"I rate the product's licensing cost a five or six on a scale of one to ten, where one is low price, and ten is high price."

"It's not costly if you configure it properly to ensure optimal performance. People don't configure it properly, which is why costs go up."

"Snowflake Analytics is not an expensive solution, and its pricing is average."

"The solution's pricing is affordable."

"On a scale of one to ten, where one is a low price, and ten is a high price, I rate the pricing a seven. The solution's pricing is high."

More Snowflake Analytics 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

14%

Retailer

10%

Financial Services Firm

Manufacturing Company

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...

See all answers

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 Snowflake Analytics?

Its performance speed is very good.

See all answers

What is your experience regarding pricing and costs for Snowflake Analytics?

We don't handle the price part. However, our customer is happy with our services regarding Snowflake Analytics.

See all answers

What needs improvement with Snowflake Analytics?

When we insert data into a large table, it takes a lot of time. This performance issue can be improved.

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

IBM InfoSphere DataStage vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

Adobe Analytics vs Snowflake Analytics

Compared 24% of the time

Mixpanel vs Snowflake Analytics

Compared 16% of the time

Amplitude vs Snowflake Analytics

Compared 13% of the time

SAP IQ vs Snowflake Analytics

Compared 10% of the time

Glassbox vs Snowflake Analytics

Compared 10% of the time

More Snowflake Analytics Competitors

Product Reports

Buyer's Guide

Azure Data Factory

December 2024

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Buyer's Guide

Snowflake Analytics

November 2024

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Microsoft

Snowflake Computing

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.

Conventional data platforms and big data solutions struggle to deliver on their fundamental purpose: to enable any user to work with any data, without limits on scale, performance or flexibility. Whether you’re a data analyst, data scientist, data engineer, or any other business or technology professional, you’ll get more from your data with Snowflake.

To achieve this, we built a new data platform from the ground up for the cloud. It’s designed with a patented new architecture to be the centerpiece for data pipelines, data warehousing, data lakes, data application development, and for building data exchanges to easily and securely share governed data. The result, A platform delivered as a service that’s powerful but simple to use.

Snowflake’s cloud data platform supports a multi-cloud strategy, including a cross-cloud approach to mix and match clouds as you see fit. Snowflake delivers advantages such as global data replication, which means you can move your data to any cloud in any region, without having to re-code your applications or learn new skills.

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

Lionsgate, Adobe, Sony, Capital One, Akamai, Deliveroo, Snagajob, Logitech, University of Notre Dame, Runkeeper

Buyer's Guide

Azure Data Factory vs. Snowflake Analytics

December 2024

Free Report: Azure Data Factory vs. Snowflake Analytics

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