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Amazon Redshift vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024
 

Categories and Ranking

Amazon Redshift
Ranking in Cloud Data Warehouse
4th
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
86
Ranking in other categories
Data Integration (1st)
 

Mindshare comparison

As of December 2024, in the Cloud Data Warehouse category, the mindshare of Amazon Redshift is 7.7%, down from 11.5% compared to the previous year. The mindshare of Azure Data Factory is 12.8%, down from 13.4% 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.
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.

Quotes from Members

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

Pros

"It's very easy to migrate from other databases to Redshift. There are migration tools dedicated for this purpose, enabling migration from other databases like MS SQL directly to Redshift. The majority of the scripts will be automatically transposed."
"Redshift's Excel features are handy. Redshift spectrum allows you to directly query the data on an Excel sheet. Now, SQL Server also allows this, but Redshift has many more features."
"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."
"Redshift is a major service of Amazon and is very scalable. It enables faster recalculations and data management, helping to retrieve data quickly."
"We have found Machine Learning use cases are very nice."
"The processing of data is very fast."
"In terms of valuable features, I like the columnar storage that Redshift provides. The storage is one of the key features that we're looking for. Also, the data updates and the latency between the data-refreshes."
"Has a very user-friendly SQL editor and it's very easy to use the connectors."
"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."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"The most important feature is that it can help you do the multi-threading concepts."
"I like the basic features like the data-based pipelines."
"The data copy template is a valuable feature."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"Allows more data between on-premises and cloud solutions"
 

Cons

"There is some missing functionality and sometimes it's so difficult to work in. We need to convert these functionalities using VACUUM inside Amazon Redshift and then it causes some complexity."
"What would make Amazon Redshift better is improvising on the pricing structure. For example, Acronis provides backups in cybersecurity, yet the pricing is a bit lesser than Amazon Redshift."
"The technical support should be better in terms of their knowledge, and they should be more customer-friendly."
"The refreshment rate of data reaching Redshift from other sources should be faster."
"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."
"The OLAP slide and dice features need to be improved."
"Running parallel queries results in poor performance and this needs to be improved."
"The solution has four maintenance windows so, when it comes to stability, I think it would be better to decrease their number."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"The product could provide more ways to import and export data."
"It can improve from the perspective of active logging. It can provide active logging information."
"Lacks a decent UI that would give us a view of the kinds of requests that come in."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"The product integration with advanced coding options could cater to users needing more customization."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."
 

Pricing and Cost Advice

"I have heard complaints about the solution’s pricing, and thus I rate it as a five."
"The price of the solution is reasonable. According to the RA3 cluster particularly, it provides 128 GB of storage with only four nodes. If you can manage your computations processes with the help of materialized views and proper queries. I think the IP clusters are very useful and overall fair for the price."
"It's pay per use. You can have multiple models."
"The solution has very competitive pricing."
"One of my customers went with Google Big Query over Redshift because it was significantly cheaper for their project."
"The price of Amazon Redshift is reasonable because it depends on the usage that you use and for DWH for the long term."
"At the moment, pricing is a little bit on the higher side, although it depends on the size of the company."
"It's not very pricey compared to other tools. I would rate the price as 5 out of 10."
"I would rate Data Factory's pricing nine out of ten."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"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."
"Data Factory is affordable."
"The solution's pricing is competitive."
"I don't see a cost; it appears to be included in general support."
"The licensing cost is included in the Synapse."
"The price you pay is determined by how much you use it."
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Top Industries

By visitors reading reviews
Educational Organization
62%
Financial Services Firm
7%
Computer Software Company
6%
Manufacturing Company
4%
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: December 2024.
824,067 professionals have used our research since 2012.