Azure Data Factory vs Snowflake Analytics comparison

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Executive Summary

We performed a comparison between Azure Data Factory and Snowflake Analytics based on real PeerSpot user reviews.

Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Azure Data Factory vs. Snowflake Analytics Report (Updated: March 2024).
765,234 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The overall performance is quite good.""UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages.""I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code.""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.""It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment.""It is beneficial that the solution is written with Spark as the back end.""I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management.""I like that it's a monolithic data platform. This is why we propose these solutions."

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"It is quite a convenient tool.""It ensures the optimization of the application development while maintaining the user-friendly nature of its UI.""The most valuable features of Snowflake for our data analytics are its time travel capability, allowing easy data recovery, and its automatic optimization of partitioning and clustering.""It can run complex workloads with varied compute.""It is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning.""The advanced features like time travel, zero copy cloning and scalability have been most useful. Snowflake requires zero maintenance for Data Warehousing on the cloud system.""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.""Snowflake Analytics is pretty easy to use with the connectors for integration with the tools and systems in my company."

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Cons
"We require Azure Data Factory to be able to connect to Google Analytics.""Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features.""Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog.""They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.""The solution needs to integrate more with other providers and should have a closer integration with Oracle BI.""There are limitations when processing more than one GD file.""Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there.""When the record fails, it's tough to identify and log."

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"Snowflake Analytics can improve the integration with machine learning tools and AI and it will make the solution more usable.""The solution’s interface is good but it could be improved.""The solution’s scalability could be improved.""One notable absence in Snowflake's offerings is an on-premises solution.""Snowflake could improve in the areas of advanced machine learning AML and generative AI.""A room for improvement in Snowflake Analytics is Spark, particularly its connector for Spark. An additional feature I'd like to see in the next release of the solution is built-in analytics.""The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms.""Machine learning should be improved."

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Pricing and Cost Advice
  • "In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
  • "This is a cost-effective solution."
  • "The price you pay is determined by how much you use it."
  • "Understanding the pricing model for Data Factory is quite complex."
  • "I would not say that this product is overly expensive."
  • "The licensing is a pay-as-you-go model, where you pay for what you consume."
  • "Our licensing fees are approximately 15,000 ($150 USD) per month."
  • "The licensing cost is included in the Synapse."
  • More Azure Data Factory Pricing and Cost Advice →

  • "Snowflake Analytics is a little more costly than Azure."
  • "When using Snowflake, you pay based on your usage. They calculate how much CPU has been used. If you use excess warehouse storage, you are charged one credit per hour. If you are in Asia, you are charged $3 per credit. If you have 10 users running parallel with the same excess, you will be charged $30."
  • "The cost of Snowflake Analytics is low, any small organization can use it."
  • "The solution's price is high and I would rate it an eight out of ten."
  • "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."
  • "It is an expensive solution, but the kind of usability and flexibility it proactively provides for the organizations justify the price."
  • "The tool is quite expensive."
  • "Snowflake Analytics is not an expensive solution, and its pricing is average."
  • More Snowflake Analytics Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:AWS Glue and Azure Data factory for ELT best performance cloud services.
    Top Answer: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 and… more »
    Top Answer: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… more »
    Top Answer:Snowflake Analytics is pretty easy to use with the connectors for integration with the tools and systems in my company.
    Top Answer:I rate the product price a seven on a scale of one to ten, where one is low price, and ten is high price. There are some additional costs based on the number of users or data warehouses.
    Top Answer:Snowflake's Snowpark is an area of concern where improvements are required. Snowflake's Snowpark is a pretty new concept. Snowflake's Snowpark should improve my adding new libraries and getting the… more »
    Ranking
    3rd
    Views
    8,490
    Comparisons
    6,535
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    40
    Average Words per Review
    495
    Rating
    8.0
    7th
    Views
    490
    Comparisons
    335
    Reviews
    24
    Average Words per Review
    419
    Rating
    8.5
    Comparisons
    Learn More
    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
    Top Industries
    REVIEWERS
    Computer Software Company35%
    Insurance Company12%
    Manufacturing Company8%
    Financial Services Firm8%
    VISITORS READING REVIEWS
    Computer Software Company13%
    Financial Services Firm13%
    Manufacturing Company8%
    Healthcare Company7%
    REVIEWERS
    Computer Software Company40%
    Financial Services Firm30%
    Outsourcing Company20%
    Retailer10%
    VISITORS READING REVIEWS
    Computer Software Company17%
    Financial Services Firm9%
    Manufacturing Company9%
    Retailer8%
    Company Size
    REVIEWERS
    Small Business28%
    Midsize Enterprise19%
    Large Enterprise53%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise13%
    Large Enterprise70%
    REVIEWERS
    Small Business24%
    Midsize Enterprise28%
    Large Enterprise48%
    VISITORS READING REVIEWS
    Small Business21%
    Midsize Enterprise18%
    Large Enterprise61%
    Buyer's Guide
    Azure Data Factory vs. Snowflake Analytics
    March 2024
    Find out what your peers are saying about Azure Data Factory vs. Snowflake Analytics and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 79 reviews while Snowflake Analytics is ranked 7th in Cloud Data Warehouse with 27 reviews. Azure Data Factory is rated 8.0, while Snowflake Analytics is rated 8.4. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Snowflake Analytics writes "A scalable tool useful for data lake and data mining processes". Azure Data Factory is most compared with Informatica PowerCenter, Alteryx Designer, Informatica Cloud Data Integration, Snowflake and Microsoft Azure Synapse Analytics, whereas Snowflake Analytics is most compared with Adobe Analytics, Mixpanel, Amplitude, Glassbox and Yellowbrick Cloud Data Warehouse. See our Azure Data Factory vs. Snowflake Analytics report.

    See our list of best Cloud Data Warehouse vendors.

    We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.