Azure Data Factory vs Snowflake Analytics comparison

Why PeerSpot?

Microsoft and Snowflake Computing are both solutions in the Cloud Data Warehouse category. Microsoft is ranked #7 with an average rating of 8.0, while Snowflake Computing is ranked #11 with an average rating of 8.5. Microsoft holds a 5.2% mindshare in CDW, compared to Snowflake Computing’s 3.4% 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 97 Azure Data Factory reviews

14,793 Views
2,663 Comparison Views

92% willing to recommend

Snowflake Analytics

Read 44 Snowflake Analytics reviews

3,529 Views
1,482 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: September 2026).

Buyer's Guide

Azure Data Factory vs. Snowflake Analytics

September 2026

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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

5.3

Azure Data Factory centralizes data, cuts costs, boosts efficiency, enhances client satisfaction, and delivers a 20-30% return.

Sentiment score

5.6

Users experience mixed ROI with Snowflake; it often improves operational efficiency, time savings, and cost control.

Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.

Deena Thayalan

Data Engineer at Vthinktechnologies

For more quotes and insights, download the Azure Data Factory report

Snowflake Analytics has positively impacted our organization by saving about eight to ten hours per week, which we can use for advanced analytics and automation tasks.

Hemanthreddy Vakiti

Junior Data engineer at a tech vendor with 10,001+ employees

For more quotes and insights, download the Snowflake Analytics report

Customer Service

Sentiment score

6.2

Azure Data Factory support is generally proficient and accessible, though response times and costs can vary for dedicated support.

Sentiment score

6.9

Snowflake Analytics support is often responsive and competent, but accessibility issues exist for non-major partners, despite strong community resources.

On a scale of one to ten, I would rate the technical support as nine.

Hafiz Abdul Mannan

Senior Consultant Oracle Technologies at a tech vendor with 10,001+ employees

The technical support from Microsoft is rated an eight out of ten.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

The technical support is responsive and helpful

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

For more quotes and insights, download the Azure Data Factory report

The Snowflake Analytics documentation is excellent.

reviewer2729628

Lead Analytics Consultant at a outsourcing company with 51-200 employees

Recently we had a two-day session where the Snowflake Analytics team provided a demo on Cortex AI and its features.

Garima Goel

Associate Principal Engineer at Nagarro

The technical support for Snowflake Analytics is excellent based on what I have heard from others.

Paresh_Nayak

Data Governance Architect at Sterlite Technologies Ltd

For more quotes and insights, download the Snowflake Analytics report

Scalability Issues

Sentiment score

7.3

Azure Data Factory is scalable and flexible but sometimes requires support for quotas and improvements for broader scalability.

Sentiment score

7.7

Snowflake Analytics offers exceptional scalability through auto-scaling, cloud integration, and efficient resource management, ideal for handling large data volumes.

Azure Data Factory is highly scalable.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

I did not experience scalability issues.

SubrataDey

Principal Data Engineer at Oracle

For more quotes and insights, download the Azure Data Factory report

Storage is unlimited because they use S3 if it is AWS, so storage has no limit.

KamleshPant

Senior Software Architect at USEReady

It supports both horizontal and vertical scaling effectively.

Paresh_Nayak

Data Governance Architect at Sterlite Technologies Ltd

Maintaining security and data governance becomes easier with an entire data lake in place, and the scalability improves performance.

Garima Goel

Associate Principal Engineer at Nagarro

For more quotes and insights, download the Snowflake Analytics report

Stability Issues

Sentiment score

7.8

Azure Data Factory is highly rated for stability, though performance varies with setup, data volume, and resource allocation.

Sentiment score

8.4

Snowflake Analytics is highly stable, supported by major cloud providers, with strong performance and minimal technical issues reported.

The solution has a high level of stability, roughly a nine out of ten.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

It gives me the accurate result.

Kunal Das

Test Engineer at Happiest Minds Technologies

I have been using Azure Data Factory for a very long time, and I did not find too many issues.

SubrataDey

Principal Data Engineer at Oracle

For more quotes and insights, download the Azure Data Factory report

Snowflake Analytics has been stable and reliable in my experience.

Garima Goel

Associate Principal Engineer at Nagarro

Snowflake Analytics is very stable; I have never experienced any crash downs or server issues.

Hemanthreddy Vakiti

Junior Data engineer at a tech vendor with 10,001+ employees

Snowflake Analytics is stable, scoring around eight point five to nine out of ten.

Paresh_Nayak

Data Governance Architect at Sterlite Technologies Ltd

For more quotes and insights, download the Snowflake Analytics report

Room For Improvement

Azure Data Factory needs better setup, connectivity, documentation, support, performance, and UI for enhanced functionality and user experience.

Snowflake Analytics struggles with data migration, integration, performance, cost issues, and needs better UI, job scheduling, and AWS support.

The ability to handle the largest volumes of data is another concern; if I have to manage more than one terabyte of data every day, I am not comfortable dealing with Azure Data Factory and had to switch to Oracle Data Integrators (ODI) because it lacks performance features.

Hafiz Abdul Mannan

Senior Consultant Oracle Technologies at a tech vendor with 10,001+ employees

If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity.

Kunal Das

Test Engineer at Happiest Minds Technologies

Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

For more quotes and insights, download the Azure Data Factory report

AIML-based SQL prompt and query generation could be an area for enhancement.

KamleshPant

Senior Software Architect at USEReady

If it offered flexibility similar to Oracle and supported more heterogeneous data sources and database connectivity, it would be even better.

Paresh_Nayak

Data Governance Architect at Sterlite Technologies Ltd

I would prefer Snowflake Analytics to improve their support response times, as sometimes the responses we receive are not very prompt and ticket assignments may not be timely.

Garima Goel

Associate Principal Engineer at Nagarro

For more quotes and insights, download the Snowflake Analytics report

Setup Cost

Azure Data Factory's pricing is competitive but complex, requiring careful monitoring to manage costs for high data usage.

Snowflake Analytics uses a pay-as-you-go model, emphasizing strategic design to manage costs, often seen as competitively priced.

The pricing is cost-effective.

Brian Sullivan

Chief Analytics Officer at Idiro Analytics

It is considered cost-effective.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

For more quotes and insights, download the Azure Data Factory report

Snowflake charges per query, which amounts to a very minor cost, such as $0.015 per query.

AyindeHammed

BI Developer at DivVerse LLC

Snowflake is better and cheaper than Redshift and other cloud warehousing systems.

KamleshPant

Senior Software Architect at USEReady

Snowflake Analytics is quite economical.

Paresh_Nayak

Data Governance Architect at Sterlite Technologies Ltd

For more quotes and insights, download the Snowflake Analytics report

Valuable Features

Azure Data Factory excels in performance, ease of use, scalability, and integration, making it highly valued for ETL processes.

Snowflake Analytics provides scalable, secure, and user-friendly analytics with cloud integration, supporting diverse data needs and flexible data sharing.

It connects to different sources out-of-the-box, making integration much easier.

Joy Maitra

Sr. Technical Architect at Hexaware Technologies Limited

The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.

Deena Thayalan

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.

KandaswamyMuthukrishnan

Director at a computer software company with 1,001-5,000 employees

For more quotes and insights, download the Azure Data Factory report

Running a considerable query on Microsoft SQL Server may take up to thirty minutes or an hour, while Snowflake executes the same query in less than three minutes.

AyindeHammed

BI Developer at DivVerse LLC

Snowflake Analytics supports data security with a single sign-on feature and complies with framework regulations, which is highly beneficial.

Paresh_Nayak

Data Governance Architect at Sterlite Technologies Ltd

Previously, we faced issues with slow queries due to traditional systems, but within Snowflake, we can assign separate virtual warehouses for reporting as well as data processing, ensuring that it does not impact tool performance and does not delay reporting to business users.

Hemanthreddy Vakiti

Junior Data engineer at a tech vendor with 10,001+ employees

For more quotes and insights, download the Snowflake Analytics report

Categories and Ranking

Azure Data Factory

Ranking in Cloud Data Warehouse

7th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Integration (5th)

Snowflake Analytics

Ranking in Cloud Data Warehouse

11th

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Web Analytics (2nd)

Mindshare comparison

As of September 2026, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 5.2%, down from 6.8% compared to the previous year. The mindshare of Snowflake Analytics is 3.4%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	5.2%
Snowflake Analytics	3.4%
Other	91.4%

Cloud Data Warehouse

Featured Reviews

Kunal Das

Test Engineer at Happiest Minds Technologies

Drag-and-drop pipelines have saved days of work and now automate data movement and backfilling

If the AI features were more improved so that I don't have to provide each and every detail, Azure Data Factory could be improved in a much better way by improving the AI features. For example, if I want to fetch any data from a raw source, I need to provide each and every detail. But if I am just uploading my raw data and if AI will sync with that data, it can analyze that data and give me proper suggestions on how that should be done in a proper way. Automatic suggestions could improve in a much better way. As I have mentioned, the AI features as well as more drag-and-drop activities could be improved. If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity. If I have used one activity, I shouldn't have to remember what activity should I use next. It should give auto-suggestions. That is why I have given a nine out of 10. Currently, I don't know about its governance and security, but in view of its improvement, I think Azure Data Factory should improve in these areas. As I already mentioned, the AI features should be improved. Also, the auto-suggestion features should also improve.

Read full review

Garima Goel

Associate Principal Engineer at Nagarro

Have created secure cloud-based data lakes and improved real-time data processing using integrated AI features

There are many capabilities which Snowflake Analytics offers that I find valuable, such as the storage and compute engine that allows working with any cloud system such as AWS or Azure, alongside its efficiencies in storage computation and cost-effectiveness, which saves money compared to on-premise systems. We also have features such as pre-cached results, Time Travel, and fail-safe, which are very useful for restoring data if deleted accidentally, and the streams and data pipes that facilitate real-time ingestion are great features as well. Snowflake Analytics offers multiple new connectors, allowing me to connect it with Kafka, and with Snowpark, I can work with any programming language such as Python, Java, or Scala for data processing and analysis. The data sharing feature offered by Snowflake Analytics is good because it allows sharing specific sets of data to end customers or users from different Snowflake Analytics accounts without exposing the entire dataset for data security reasons. Snowflake Analytics' support for machine learning models and real-time insights has enhanced significantly. Originally, it wasn't strong in AI/ML, but now it has multiple models and forecasting capabilities, providing good competition to tools such as Databricks and Spark. In BI, I have worked majorly with Microsoft Power BI, and the integration with Snowflake Analytics is very easy. The way we integrate Snowflake Analytics with other on-premise systems just requires the warehouse details, username, passwords, and the account name, along with multiple options such as client ID and credentials for logging in and creating a session. The end-to-end encryption provided by Snowflake Analytics is very important because, in my previous firm, working in finance and investment management, data encryption is necessary due to the sensitive nature of customer data and the involvement of people's money. It's crucial to have encryption in transit and at rest, along with data masking features which Snowflake Analytics offers.

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

Computer Software Company

Construction Company

17%

Outsourcing Company

14%

Financial Services Firm

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	64

By reviewers
Company Size	Count
Small Business	11
Midsize Enterprise	13
Large Enterprise	23

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 is your experience regarding pricing and costs for Snowflake Analytics?

The pricing for Snowflake Analytics is reasonable, but as I am not part of the management team, I am not certain about our organization's exact costs. Overall, the return on investment for this too...

See all answers

What needs improvement with Snowflake Analytics?

One improvement Snowflake Analytics could benefit from is in cost, particularly during peak hours. Sometimes, due to its automatic scalability, we think it has scaled up, but it does not always hap...

See all answers

What is your primary use case for Snowflake Analytics?

I am using Snowflake Analytics because we are already using Snowflake for data engineering and data warehousing tasks, and we are using it for analytics as well as business intelligence reporting. ...

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

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

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

SnapLogic vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Amplitude vs Snowflake Analytics

Compared 12% of the time

BigQuery vs Snowflake Analytics

Compared 8% of the time

Keen IO vs Snowflake Analytics

Compared 6% of the time

Firebolt vs Snowflake Analytics

Compared 5% of the time

Adobe Analytics vs Snowflake Analytics

Compared 5% of the time

More Snowflake Analytics Competitors

Product Reports

Buyer's Guide

Azure Data Factory

September 2026

Download Azure Data Factory product report

Buyer's Guide

Snowflake Analytics

August 2026

Download Snowflake Analytics product report

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.

Microsoft

Snowflake Analytics offers advanced capabilities in data warehousing and cloud data migration, with support for machine learning and business intelligence tasks. Its scalable architecture supports large data volumes while enhancing cost efficiency through decoupled computation and storage.

As a flexible, managed environment, Snowflake Analytics enhances data sharing and integration across multiple cloud platforms. It allows seamless data pipeline creation, supports advanced analytics, and facilitates reporting and visualization. Despite facing integration challenges with legacy systems and complex queries, Snowflake's continuous improvements aim to address these issues, making it a reliable choice for organizations transitioning to the cloud.

What features define Snowflake Analytics?

Time Travel: Enables users to access historical data effortlessly.
Zero-Copy Cloning: Allows cloning of data without storage costs.
Scalability: Easily handles large data volumes.
Data Sharing: Facilitates seamless data sharing across platforms.
Decoupled Architecture: Enhances cost efficiency by separating computation and storage.

What benefits should businesses look for in Snowflake reviews?

Cost Efficiency: Optimized costs through advanced storage management.
Performance: High-speed computations and real-time analytics.
Security: End-to-end encryption ensuring data security.
Flexibility: Supports multiple cloud environments for diverse needs.

Enterprises across industries utilize Snowflake Analytics for its robust data handling and cloud integration capabilities. It serves sectors in need of efficient data warehousing, real-time analytics, and machine learning support, making it suitable for cloud migration and enhancing business intelligence operations.

Snowflake Computing

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

September 2026

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