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."It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."
"We use AWS Lake Formation typically for the data warehouse."
"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."
"The solution has many features that are applicable to events such as audits."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
"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."
"The solution has a good interface and the integration with GitHub is very useful."
"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."
"Data Factory's most valuable feature is Copy Activity."
"It makes it easy to collect data from different sources."
"We haven't had any issues connecting it to other products."
"An excellent tool for pipeline orchestration."
"AWS Lake Formation's pricing could be cheaper."
"For the end-users, it's not as user-friendly as it could be."
"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."
"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."
"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."
"There aren't many third-party extensions or plugins available in the solution."
"The support and the documentation can be improved."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"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."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"Some of the optimization techniques are not scalable."
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.
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