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."Redshift Spectrum is the most valuable feature."
"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."
"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."
"The valuable features are performance, data compression, and scalability."
"The most valuable feature is the scalability, as it grows according to our needs."
"The most valuable features are that it's easy to set up and easy to connect the many tools that connect to it."
"I like the cost-benefit ratio, meaning that it is as easy to use as it is powerful and well-performing."
"The solution has very competitive pricing."
"The most valuable feature of this solution would be ease of use."
"I am one hundred percent happy with the stability."
"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."
"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."
"We have been using drivers to connect to various data sets and consume data."
"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."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"Planting is the primary key enforcement that should be improved."
"Should be made available across zones, like other Multi-AZ solutions."
"The solution is unable to work fast."
"It would be nice if we could turn off an instance. However, it would retain the instance in history, thus allowing us to restart without beginning from scratch."
"They should provide a better way to work with interim data in a structured way than to store it in parquet files locally."
"The solution has four maintenance windows so, when it comes to stability, I think it would be better to decrease their number."
"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."
"The product could be improved by making it more flexible."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"Lacks a decent UI that would give us a view of the kinds of requests that come in."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"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."
"The product could provide more ways to import and export data."
"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."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
Amazon Redshift is ranked 4th in Cloud Data Warehouse with 58 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 AWS Lake Formation, Snowflake, Teradata, 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.
