Azure Data Factory vs Snowflake Analytics comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Number of Reviews
81
Ranking in other categories
Data Integration (1st)
Snowflake Analytics
Ranking in Cloud Data Warehouse
6th
Average Rating
8.4
Number of Reviews
31
Ranking in other categories
Web Analytics (1st)
 

Mindshare comparison

As of June 2024, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 13.6%, up from 13.4% compared to the previous year. The mindshare of Snowflake Analytics is 0.2%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
Unique Categories:
Data Integration
9.6%
Web Analytics
7.0%
 

Featured Reviews

Zubair_Ahmed - PeerSpot reviewer
Nov 30, 2023
Seamless cloud-based data integration providing a versatile platform with scalable data processing, diverse data connectors, and comprehensive monitoring and management capabilities
My task involves extracting data from a source, performing necessary transformations, and subsequently loading the data into a target destination, which happens to be Azure SQL Database The company is experiencing significant benefits as one of our customers is successfully implementing the…
KR
Mar 6, 2024
Enhanced our data warehousing capabilities, learning curve is very small and data sharing and data sampling are very easy
There are a few areass of improvement. * Databricks may be superior for machine learning, Snowflake is still maturing in that area. Also, machine learning in Snowflake isn't as advanced as in other products. I haven't heard of any successful industry-wide use cases of machine learning implemented in Snowflake. It might take a couple of years to reach the same level as Databricks. * Python integration is still an upcoming capability within Snowflake. * Additionally, loading data from the cloud to Snowflake is straightforward, but we still lack the capability to do the reverse – from Snowflake to the cloud.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"An excellent tool for pipeline orchestration."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"We have found the bulk load feature very valuable."
"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"We haven't had any issues connecting it to other products."
"Time Travel and Snowpipe are good features."
"Very good flexibility and it offers computation completely decoupled from the storage."
"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 is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning."
"The computational power of Snowflake is very good."
"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 performance has been good."
"The most valuable feature of Snowflake Analytics is its performance."
 

Cons

"The support and the documentation can be improved."
"I would like to be informed about the changes ahead of time, so we are aware of what's coming."
"The one element of the solution that we have used and could be improved is the user interface."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"The pricing model should be more transparent and available online."
"It can improve from the perspective of active logging. It can provide active logging information."
"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."
"The number of standard adaptors could be extended further."
"End-to-end execution of jobs isn't possible with Snowflake, which means we have to do some customization."
"Snowflake could improve in the areas of advanced machine learning AML and generative AI."
"The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms."
"Snowflake's Snowpark is an area of concern where improvements are required."
"The platform's data governance space needs more capability."
"Implementing everything on-premise is challenging because it require proper support from advisors, DBAs, and others."
"The distribution methodology isn't as strong as Bethesda or SAP HANA. It's not as strong as other competitors."
"The tool should support EIM use cases. I guess the product is already working on it. I look forward to seeing inbuilt AI generative tools in the solution's future releases. The tool's price can be a little lower. The solution's on-premises support is also very limited. We have to rely on other support services to deploy it on-premises."
 

Pricing and Cost Advice

"Data Factory is affordable."
"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"It's not particularly expensive."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"ADF is cheaper compared to AWS."
"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."
"The cost is based on the amount of data sets that we are ingesting."
"The cost of Snowflake Analytics is low, any small organization can use it."
"I rate the product price a seven on a scale of one to ten, where one is low price, and ten is high price."
"It is an expensive solution, but the kind of usability and flexibility it proactively provides for the organizations justify the price."
"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."
"Snowflake Analytics is not an expensive solution, and its pricing is average."
"The solution's price is high and I would rate it an eight out of ten."
"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."
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Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
13%
Manufacturing Company
8%
Healthcare Company
7%
Computer Software Company
16%
Financial Services Firm
9%
Manufacturing Company
9%
Retailer
8%
 

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 is your experience regarding pricing and costs for Snowflake Analytics?
The pricing is on the higher side. I would rate it seven out of ten.
What needs improvement with Snowflake Analytics?
Some functionalities available through SQL are inaccessible in Python or Java within Snowflake. Snowflake must enhance its support for these programming languages to offer full flexibility and capa...
 

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
Lionsgate, Adobe, Sony, Capital One, Akamai, Deliveroo, Snagajob, Logitech, University of Notre Dame, Runkeeper
Find out what your peers are saying about Azure Data Factory vs. Snowflake Analytics and other solutions. Updated: May 2024.
787,779 professionals have used our research since 2012.