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Azure Data Factory vs Snowflake comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Oct 8, 2024
 

Categories and Ranking

Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
86
Ranking in other categories
Data Integration (1st)
Snowflake
Ranking in Cloud Data Warehouse
1st
Average Rating
8.4
Reviews Sentiment
7.5
Number of Reviews
98
Ranking in other categories
Data Warehouse (1st)
 

Mindshare comparison

As of November 2024, in the Cloud Data Warehouse category, the mindshare of Azure Data Factory is 12.9%, down from 13.4% compared to the previous year. The mindshare of Snowflake is 28.7%, up from 23.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Thulani David Mngadi - PeerSpot reviewer
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.
VivekSingh 1 - PeerSpot reviewer
Provides good data ingestion capability, but should include more AI capabilities
The solution's integration aspect is good, and all the connectors are in place. I found Snowflake similar to RDS. We use it for both data in motion and data in transit. It looks like the tool handles the data quite securely. We create ETL patterns. We ingest data from different source systems, and we have to create data pipelines. It would be useful if we could have AI features added to identify what I'm going to do with this data. It would be good if it could look at the data and help me create an automated pipeline instead of me creating a pipeline by myself. I'm from a retail background. I completed my Oracle DBA training a long time ago, about 18 years ago. I was quite familiar with the Snowflake and relational database concepts since I had already completed the Oracle ops, DBA ops, OCP, and OPA courses. For me, it was a journey similar to when I shifted from Oracle RDS to Snowflake. Although I was quite familiar with most of the concepts, there were some learnings. Whosoever is in the data field should at least try Snowflake once. They will then realize the best features in the solution and can continue using it. Overall, I rate the solution a seven out of ten.

Quotes from Members

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

Pros

"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"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."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"Its integrability with the rest of the activities on Azure is most valuable."
"Very easy to use and easy to query."
"Everything is automatic, and I don't have to do any maintenance."
"We find the data sharing and data marketplace aspects of Snowflake absolutely amazing."
"The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power."
"The solution is very stable."
"The most valuable feature has been the Snowflake data sharing and dynamic data masking."
"It is quite easy to manage."
"A user-friendly and reliable solution."
 

Cons

"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"The setup and configuration process could be simplified."
"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."
"The deployment should be easier."
"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."
"Data Factory's monitorability could be better."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"The support and the documentation can be improved."
"From the documentation, the black box is not very descriptive. Snowflake does not reveal how exactly the data is processed or sourced."
"The design of the product is easily misunderstood."
"Its transaction application needs improvement."
"I see room for improvement when it comes to credit performance. The other thing I'd like to be improved is the warehouse facility."
"I have heard people having difficulty with the machine learning model, so there may be room for improvement."
"Snowflake has to improve their spatial parts since it doesn't have much in terms of geo-spatial queries."
"We are yet to figure out how to integrate tools, such as Liquibase, to release changes to our data warehouse model."
"The pricing of the solution should be much easier to calculate or find by yourself."
 

Pricing and Cost Advice

"Understanding the pricing model for Data Factory is quite complex."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"The solution is cheap."
"I would not say that this product is overly expensive."
"The pricing model is based on usage and is not cheap."
"Data Factory is expensive."
"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."
"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."
"The solution is costly, making it unsuitable for midsize organizations due to its price."
"Currently, we have a trial account, so we don't need a license. After our project starts, we would need a permanent license."
"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."
"There is a licensing for this solution and we purchased an enterprise license. Overall the solution is cost-effective."
"The price for the solution's license depends on the use cases."
"Snowflake is cost-effective."
"I am not much aware of the price, but based on what I have analyzed so far, its cost is reasonable as compared to on-prem data warehouse solutions. It provides a great deal for production."
"We used Snowflake to see if it is cheaper than using BigQuery. It was just to maintain the cost or the KPI regarding the cost of connectivity by users. Snowflake wasn't cheaper than BigQuery, and its affordability was the main issue."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Educational Organization
35%
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
6%
 

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 pricing part is based on the computing and storage. The costs are different and then there are services costs as well. I have heard that Snowflake is costlier than Redshift or GCP BigQuery. A s...
What needs improvement with Snowflake?
I think people do not want to create pipelines for many customers now. Normally, we have this layer architecture, like layer one, layer two, layer three, or layer four, where we have raw data, inte...
 

Also Known As

No data available
Snowflake Computing
 

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
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: October 2024.
816,406 professionals have used our research since 2012.