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."I find the most valuable features to be the MPP style of processing, which mostly all of the data warehouses provide. The ability to integrate all other AWS services, such as NSS and S3, with little effort is very helpful. The service is well maintained, there are update patches frequently."
"Redshift is a major service of Amazon and is very scalable. It enables faster recalculations and data management, helping to retrieve data quickly."
"Redshift COPY command, because much of my work involved helping customers migrate large amounts of data into Redshift."
"The most valuable feature of Amazon Redshift is its ability to handle really large sets of data."
"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 solution is scalable. It handles different loads very well."
"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."
"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."
"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 this solution would be ease of use."
"The security of the agent that is installed on-premises is very good."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"Allows more data between on-premises and cloud solutions"
"The best part of this product is the extraction, transformation, and load."
"The function of the solution is great."
"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."
"The explain panel in the Redshift database could be better."
"The solution has four maintenance windows so, when it comes to stability, I think it would be better to decrease their number."
"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."
"Running parallel queries results in poor performance and this needs to be improved."
"It takes a lot of time to ingest and update the data."
"For people who struggle with IAM or role-based management, the setup isn't easy."
"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."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"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."
"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."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"The initial setup is not very straightforward."
"Some known bugs and issues with Azure Data Factory could be rectified."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
Amazon Redshift is ranked 4th in Cloud Data Warehouse with 61 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 Teradata, Vertica, Snowflake, Microsoft Azure Synapse Analytics and Amazon EMR, whereas Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage. See our Amazon Redshift vs. Azure Data Factory report.
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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.
