Azure Data Factory vs Snowflake comparison


Comparison Buyer's Guide

Executive SummaryUpdated on Mar 6, 2024

Categories and Ranking

Azure Data Factory
Ranking in Cloud Data Warehouse
Average Rating
Number of Reviews
Ranking in other categories
Data Integration (1st)
Ranking in Cloud Data Warehouse
Average Rating
Number of Reviews
Ranking in other categories
Data Warehouse (1st)

Mindshare comparison

As of July 2024, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 11.0%, down from 12.2% compared to the previous year. The mindshare of Snowflake is 30.4%, up from 24.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
Unique Categories:
Data Integration
Data Warehouse

Q&A Highlights

Oct 04, 2023

Featured Reviews

Mar 6, 2024
Offers good integration with SQL pools and serverless architecture
We have multiple banking applications running on SSIS pipelines. We're in the process of upgrading them to a hybrid cloud architecture. For that, we use Azure Data Factory to move data from on-premises to the cloud – mainly for back-end database operations and ETL transformations. We primarily use it to load data from an on-premises SQL Server to either Blob storage or an Azure SQL data warehouse. For other integrations, especially those outside of Azure, we tend to use Informatica Cloud Services (ICS). For structured data loading, we use it However, we use Informatica for unstructured or semi-structured data. We also use Snowflake for ETL processes and sometimes for streaming. In my opinion, ADF isn't as suitable for streaming – for streaming, Snowflake streamlets or Informatica structured streaming are more reliable. ADF works well for batch processing, though.
Azhagarasan Annadorai - PeerSpot reviewer
Apr 30, 2023
Optimizes costs, works with various clouds, and great dashboards
Data warehousing is typically a rich guys' toy. Large enterprises are only able to leverage data warehouses for data analytics purposes. We wanted to change that and wanted to build a data warehouse template model for businesses across industries.  If Snowflake was not around, we would have used…

Quotes from Members

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


"We use the solution to move data from on-premises to the cloud."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"Powerful but easy-to-use and intuitive."
"We haven't had any issues connecting it to other products."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"We have been using drivers to connect to various data sets and consume data."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The technical support on offer is excellent."
"The solution is easy to use."
"The integration capabilities of the product are good and you get what you pay for when it comes to Snowflake."
"Scaling is a big plus point of Snowflake."
"The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management. It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure."
"The most valuable feature of Snowflake is its performance. We can access the data quickly. Additionally, it handles structured and non-structured data."
"The ETL and data ingestion capabilities are better in this solution as compared to SQL Server. SQL Server doesn't do much data ingestion, but Snowflake can do it quite conveniently."
"Once you have finished your designs they can be easily imported to Snowflake and the information can be readily accessed without an IT expert."


"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"There aren't many third-party extensions or plugins available in the solution."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"The speed and performance need to be improved."
"Some of the optimization techniques are not scalable."
"Snowflake can improve its machine learning and AI capabilities."
"Snowflake has support for stored procedures, but it is not that powerful."
"The scheduling system can definitely be better because we had to use external airflow for that. There should be orchestration for the scheduling system. Snowflake currently does not support machine learning, so it is just storage. They also need some alternatives for SQL Query. There should also be support for Spark in different languages such as Python."
"Support needs improvement, as it can take several days before you get some initial support."
"I think that Snowflake could improve its user interface. The current one is not interactive."
"They have a new console, but I couldn't figure out anything in the new console. So, if I shift to the old console, I can figure out where to create the database schema and other things, but I have no idea where to go in the new console. That's one thing they can improve. I don't know why they created a new console to confuse. The old, classic console is much better."
"The documentation could improve. They should provide architecture information."
"Some SQL language functions could be included."

Pricing and Cost Advice

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"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."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"It's not particularly expensive."
"ADF is cheaper compared to AWS."
"The price is fair."
"The solution's pricing is competitive."
"It is per credit. It has a use-it-as-you-go model. We bought a chunk of 20,000 credits, and they were lasting us for at least a year. We didn't have the scale of data like a much larger company to consume more credits. For us, it was very inexpensive. Their strategy is just to leverage what you've got and put Snowflake in the middle. It doesn't make it expensive because most of the organizations already have reporting tools. Now, if you were starting from scratch, it might be cheaper to go a different way."
"I have worked with multiple clouds, and cost-wise, it is a bit costlier than others, such as Redshift. Its price should be reduced."
"It is on a monthly basis. It is based on your usage. There are no additional costs from the point of the licensing fee. We do give some kind of evaluation to the customers about how much it is going to be. You can decide in Snowflake the virtual machine that you are using for customers. There are several kinds of virtual machines that you can use. It is similar to the clothing sizes: small to extra large. If you need more power in the coming month, you can decide in advance and take a more powerful machine. You can just select it from the platform. You can also decide which machine you want to take for extracting data."
"The pricing is economical as compared to traditional solutions like Oracle and competitive pricing."
"On average, with the number of queries that we run, we pay approximately $200 USD per month."
"Snowflake goes by credits. For a financial institution where you have 5,000 employees, monthly costs may run up to maybe $5,000 to $6,000. This is actually based on the usage. It is mostly the compute cost. Your computing cost is the variable that is actually based on your usage. It is pay-per-use. In a pay-per-use case, you won't be spending more than $6,000 to $7,000 a month. It is not more than that for a small or medium enterprise, and it may come down to $100K per year. Storage is very standard, which is $23 a terabyte. It is not much for any enterprise. If you have even 20 terabytes, you are not spending more than $400 per month, which may turn out to be $2,000 to $3,000 per annum."
"Pricing is based on usage. It is the most expensive of our data tools."
"Snowflake is expensive, but when I consider what we get for that price, it's fair. I rate the solution three out of five for affordability, right in the middle."
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Top Industries

By visitors reading reviews
Computer Software Company
Financial Services Firm
Manufacturing Company
Healthcare Company
Educational Organization
Financial Services Firm
Computer Software Company
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.
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 do you like most about Snowflake?
The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power.
What is your experience regarding pricing and costs for Snowflake?
The product's price range falls between average to a bit expensive range. I think the tool is worth the money if you use it properly. It is difficult for me to speak about the number of users who u...
What needs improvement with Snowflake?
I don't think that the AI tools in Snowflake are good. AI tools in Snowflake can be improved. Even if the AI tools in Snowflake are good, I feel that it would be expensive. The cost of the AI part ...

Also Known As

No data available
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
Accordant Media, Adobe, Kixeye Inc., Revana, SOASTA, White Ops
Find out what your peers are saying about Azure Data Factory vs. Snowflake and other solutions. Updated: May 2024.
792,098 professionals have used our research since 2012.