We performed a comparison between AWS Lake Formation 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 solution is quite good at handling analytics. It's done a good job at helping us centralize them."
"The most important advantage in using AWS Lake Formation is its ability to connect the data lake to the other technologies in AWS. This is what I advise my clients."
"We use AWS Lake Formation typically for the data warehouse."
"The solution has many features that are applicable to events such as audits."
"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."
"We have been using drivers to connect to various data sets and consume data."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"The most valuable aspect is the copy capability."
"The user interface is very good. It makes me feel very comfortable when I am using the tool."
"The solution could make improvements around orchestration and doing some automation stuff on AWS front automation. It would be useful if we could use automation to build images and use hardened images which are CIS compliant."
"AWS Lake Formation's pricing could be cheaper."
"For the end-users, it's not as user-friendly as it could be."
"In our experience what could be improved are not the support, performance or monitoring, but at a managerial level, the very expensive professional services of AWS. This could be an area of improvement for them. It's too expensive to acquire their support."
"It falls short when it comes to more granular access control, such as cell-level or row-level entitlements which is a significant drawback for organizations that require precise control over who can access specific rows of data."
"Lacks in-built streaming data processing."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"The solution needs to be more connectable to its own services."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"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."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"Azure Data Factory uses many resources and has issues with parallel workflows."
AWS Lake Formation is ranked 12th in Cloud Data Warehouse with 5 reviews while Azure Data Factory is ranked 3rd in Cloud Data Warehouse with 81 reviews. AWS Lake Formation is rated 7.6, while Azure Data Factory is rated 8.0. The top reviewer of AWS Lake Formation writes "Strategically aligning data management in a multi-cloud environment with significant reporting challenges". 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". AWS Lake Formation is most compared with Snowflake, Amazon Redshift, Microsoft Azure Synapse Analytics, BigQuery and Amazon EMR, whereas Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Denodo. See our AWS Lake Formation 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.
