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Amazon Redshift vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

Review summaries and opinions

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

Categories and Ranking

Amazon Redshift
Ranking in Cloud Data Warehouse
5th
Average Rating
7.8
Reviews Sentiment
6.9
Number of Reviews
67
Ranking in other categories
No ranking in other categories
Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
89
Ranking in other categories
Data Integration (1st)
 

Mindshare comparison

As of February 2025, in the Cloud Data Warehouse category, the mindshare of Amazon Redshift is 6.8%, down from 9.7% compared to the previous year. The mindshare of Azure Data Factory is 9.1%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Ved Prakash Yadav - PeerSpot reviewer
Works as a data warehouse system and collects data from different sources
In terms of improvement, I believe Amazon Redshift could work on reducing its costs, as they tend to increase significantly. Additionally, there are occasional issues with nodes going down, which can be problematic. We often encounter issues like someone dropping a column or changing the order of columns, which can cause synchronization problems when pushing data through our pipeline. It's a minor issue, but it can be annoying.
Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.

Quotes from Members

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

Pros

"I like it because the usage is very similar to Microsoft SQL server. The structure of the query and the temporary tables are very similar."
"I like the cost-benefit ratio, meaning that it is as easy to use as it is powerful and well-performing."
"Though Amazon Redshift is good, it depends on what kind of business you're trying to do, what type of analytics you need, and how much data you have."
"The stability of Amazon Redshift is good."
"Redshift allows you to transform different data formats and consolidate them into one Redshift cluster. This means you can transform various siloed data sources like Excel files and CSV files into Redshift."
"The most valuable feature of Redshift is its cluster."
"Its simplicity in configuration, cost-effectiveness due to being in the cloud and close to our data sources, and the fact that it's a managed service that is scalable and reliable are highly valuable."
"You can copy JSON to the column and have it analyzed using simple functions."
"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."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."
"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"I can do everything I want with SSIS and Azure Data Factory."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
 

Cons

"Redshift's GUI could be more user-friendly. It's easier to perform queries and all that stuff in Azure Synapse Analytics."
"Amazon Redshift does not have the capability to dynamically increase the VM file."
"Amazon Redshift could improve the user interface support."
"Amazon should provide more cloud-native tools that can integrate with Redshift like Microsoft's development tools for Azure."
"Running parallel queries results in poor performance and this needs to be improved."
"I would like to improve the pricing and the simplicity of using this solution."
"AWS Snowflake has a very good feature for cloning databases. It makes it easy to clone a data warehouse, which is useful. I would like to see this feature in Redshift."
"Query compilation time needs a lot of improvement for cases where you are generating queries dynamically."
"The support and the documentation can be improved."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"The deployment should be easier."
"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."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"The Microsoft documentation is too complicated."
 

Pricing and Cost Advice

"BI is sold to our customer base as a part of the initial sales bundle. A customer may elect to opt for a white labeled site for an up-charge."
"Amazon Redshift is an expensive solution. Larger organizations can afford this solution, but smaller businesses would struggle to afford it."
"If you want a fixed price, an to not worry about every query, but you need to manage your nodes personally, use Redshift."
"The part that I like best is that you only pay for what you are using."
"Redshift is very cost effective for a cloud based solution if you need to scale it a lot. For smaller data sizes, I would think about using other products."
"It's pay per use. You can have multiple models."
"The product is cheap considering what it provides; I rate it five out of five for affordability."
"The cost will depend on how you set up your warehouse and what kind of data you store."
"Data Factory is expensive."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."
"The pricing is a bit on the higher end."
"Pricing appears to be reasonable in my opinion."
"The price is fair."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"The solution's pricing is competitive."
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Top Industries

By visitors reading reviews
Educational Organization
64%
Financial Services Firm
7%
Computer Software Company
5%
Manufacturing Company
3%
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

How does Amazon Redshift compare with Microsoft Azure Synapse Analytics?
Amazon Redshift is very fast, has a very good response time, and is very user-friendly. The initial setup is very straightforward. This solution can merge and integrate well with many different dat...
What do you like most about Amazon Redshift?
The tool's most valuable feature is its parallel processing capability. It can handle massive amounts of data, even when pushing hundreds of terabytes, and its scaling capabilities are good.
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...
 

Overview

 

Sample Customers

Liberty Mutual Insurance, 4Cite Marketing, BrandVerity, DNA Plc, Sirocco Systems, Gainsight, Blue 449
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
Find out what your peers are saying about Amazon Redshift vs. Azure Data Factory and other solutions. Updated: January 2025.
832,138 professionals have used our research since 2012.