We performed a comparison between Amazon Redshift and Azure Data Factory based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The ability to reload data multiple times at different times."
"The most valuable feature is its scalability."
"The most valuable feature is the scalability, as it grows according to our needs."
"The initial setup is easy."
"Redshift is a major service of Amazon and is very scalable. It enables faster recalculations and data management, helping to retrieve data quickly."
"If the analyst knows SQL, which is comfortable and easy to use to go between all of these tool stacks, I think it's reliable. It's a secure and reliable data warehouse."
"The main benefit is that our portal for end users is running in AWS, so we can easily connect it to other AWS services."
"Setup is easy. It's a fast solution with machine learning features, good integration, and a good API."
"Powerful but easy-to-use and intuitive."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."
"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 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."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"I can do everything I want with SSIS and Azure Data Factory."
"We are using third-party tools to integrate Amazon Redshift, they should create their own interface on their own for it to be easily connected on the AWS itself."
"There are physically too many pipelines for a company of this size to maintain. For a data scientist, it's very difficult to learn the data in all of these different environments."
"Amazon Redshift could improve the user interface support."
"I would like to improve the pricing and the simplicity of using this solution."
"Infinite storage is available in Snowflake and is not available in Redshift."
"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 recently moved from the DC2 cluster to the RA3 cluster, which is a different node type and we are finding some issues with the RA3 cluster regarding connection and processing. There is room for improvement in this area. We are in talks with AWS regarding the connection issues."
"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 pricing scheme is very complex and difficult to understand."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"Data Factory's monitorability could be better."
"It would be better if it had machine learning capabilities."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"Data Factory's performance during heavy data processing isn't great."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"There aren't many third-party extensions or plugins available in the solution."
Amazon Redshift is ranked 4th in Cloud Data Warehouse with 60 reviews while Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews. Amazon Redshift is rated 7.8, while Azure Data Factory is rated 8.0. The top reviewer of Amazon Redshift writes "Provides one place where we can store data, and allows us to easily connect to other services with AWS". On the other hand, the top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". Amazon Redshift is most compared with Snowflake, Teradata, AWS Lake Formation, Vertica and Amazon EMR, whereas Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics. See our Amazon Redshift vs. Azure Data Factory report.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.