AWS Lake Formation vs Azure Data Factory comparison

"AWS Lake Formation works hand in hand with other products."

"AWS Lake Formation lets you see all your data and tables on one screen."

"We use AWS Lake Formation typically for the data warehouse."

"It is seamlessly integrated within the AWS ecosystem, making it straightforward to manage access patterns for AWS-native services."

"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."

"The solution is quite good at handling analytics. It's done a good job at helping us centralize them."

"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."

"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."

"In terms of my personal experience, it works fine."

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."

"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 most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."

"The function of the solution is great."

"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."

"You need to have data experience to use the product."

"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."

"AWS Lake Formation's pricing could be cheaper."

"Lake Formation could enhance its capabilities in audit logs, real-time monitoring, and advanced data governance."

"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."

"The pricing scheme is very complex and difficult to understand."

"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."

"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."

"Azure Data Factory uses many resources and has issues with parallel workflows."

"There aren't many third-party extensions or plugins available in the solution."

"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."

"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."

"Data Factory's monitorability could be better."

"AWS Lake Formation is a bit expensive."

"Understanding the pricing model for Data Factory is quite complex."

"The cost is based on the amount of data sets that we are ingesting."

"The licensing cost is included in the Synapse."

"Data Factory is expensive."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."