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Amazon Redshift vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Amazon Redshift
Ranking in Cloud Data Warehouse
4th
Average Rating
7.8
Reviews Sentiment
7.4
Number of Reviews
66
Ranking in other categories
No ranking in other categories
Azure Data Factory
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
86
Ranking in other categories
Data Integration (1st)
 

Mindshare comparison

As of November 2024, in the Cloud Data Warehouse category, the mindshare of Amazon Redshift is 7.7%, down from 11.9% compared to the previous year. The mindshare of Azure Data Factory is 12.9%, 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

"The most valuable features of Amazon Redshift are that its fast and efficient. We have lots of TBs of data and it's very fast."
"The most valuable feature of Amazon Redshift is its ability to handle really large sets of data."
"The ability to reload data multiple times at different times."
"The initial setup of this solution is straightforward."
"It is quite simple to use and there are no issues with creating the tables."
"The solution's flexibility is its most valuable feature. It's also easy to scale and has relatively painless pricing."
"Amazon Redshift is a really powerful database system for reporting and data warehousing."
"This service can merge and integrate well with all databases."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"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."
"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."
"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."
"Data Factory's most valuable feature is Copy Activity."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
 

Cons

"I would like to improve the pricing and the simplicity of using this solution."
"In our experiments, the handling of unstructured data was not very smooth."
"Migrating data from other data sources can be challenging when you are working with multibyte character sets."
"The customer support could be more responsive."
"In the next release, a pivot function would be a big help. It could save a lot of time creating a query or process to handle operations."
"If you require a highly scalable solution, I would not recommend Amazon Redshift."
"One area where Amazon Redshift could improve is in adopting the compute-separate, data-separate architecture, which Delta, Snowflake are adopting, and a few others in the cloud data warehouse spectrum."
"It would be useful to have an option where all of the data can be queried at once and then have the result shown."
"The speed and performance need to be improved."
"The product integration with advanced coding options could cater to users needing more customization."
"Data Factory's cost is too high."
"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 main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."
"It would be better if it had machine learning capabilities."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
 

Pricing and Cost Advice

"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."
"On a scale of one to ten, where one is a low price and ten is a high price, I rate the pricing a seven."
"One of my customers went with Google Big Query over Redshift because it was significantly cheaper for their project."
"The product is quite expensive."
"If you want a fixed price, an to not worry about every query, but you need to manage your nodes personally, use Redshift."
"Pricing for Amazon Redshift is reasonable, though it could be somewhat higher than other solutions, such as Azure. Still, when you base your comparison on the services offered and the pricing, it's the most reasonable versus its competitors, such as RDS."
"My customers have implementations that cost about $500 a month for a very small one. I also have a customer with a monthly invoice of about $25,000 USD."
"The best part about this solution is the cost."
"Pricing is comparable, it's somewhere in the middle."
"The cost is based on the amount of data sets that we are ingesting."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"The solution's pricing is competitive."
"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"Product is priced at the market standard."
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
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Top Industries

By visitors reading reviews
Educational Organization
60%
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.
What is your experience regarding pricing and costs for Amazon Redshift?
You can start small with a basic cluster to learn and practice with it. Selecting the most basic and economical cluster type can save you enough money to move forward with the solution or go with a...
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: October 2024.
816,406 professionals have used our research since 2012.