Azure Data Factory vs Census comparison

"This is an excellent tool for pipeline orchestration; connecting the different components and activities as well as gathering data."

"The initial setup is pretty simple and it can be deployed in a couple of hours."

"I like how you can create your own pipeline in your space and reuse those creations."

"We have used other ETL solutions in the past, and Azure Data Factory is the best one."

"Azure Data Factory is a very easy to use tool."

"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 user interface is very good; it makes me feel very comfortable when I am using the tool."

"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."

"Through automation, we have been able to save between forty and sixty percent in time and cost, thanks to the automation platform and automation capabilities."

"Based on delivery metrics and team feedback, the benefit is that the time to activate new use cases was reduced from weeks to days, engineering effort dedicated to reverse ETL pipelines decreased by sixty to seventy percent, maintenance overhead for custom integration was almost completely eliminated, and marketing and growth teams accelerated experimentation cycles significantly."

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."

"It does not appear to be as rich as other ETL tools. It has very limited capabilities."

"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"The setup and configuration process could be simplified."

"When you raise an issue, sometimes the people who are available are unfamiliar with that particular technology, so they have to route the issue to the concerned person."

"Some prebuilt data source or data connection aspects are generic."

"One area that may need improvement is that sometimes a custom setup is very difficult to configure."

"The areas for improvement are complex transformation logic because it must still be handled upstream in the warehouse, and cost predictability requires attention at high data volumes."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"The pricing model is based on usage and is not cheap."

"I don't see a cost; it appears to be included in general support."

"It's not particularly expensive."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"I would not say that this product is overly expensive."