Azure Data Factory vs TIBCO Scribe comparison

"The most valuable aspect is the copy capability."

"I am one hundred percent happy with the stability."

"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."

"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."

"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."

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

"The most valuable feature is the ease in which you can create an ETL pipeline."

"Azure Data Factory became more user-friendly when data-flows were introduced."

"The most valuable feature of TIBCO Scribe is the connectors available to various products."

"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."

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

"The speed and performance need to be improved."

"Some of the optimization techniques are not scalable."

"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."

"It can improve from the perspective of active logging. It can provide active logging information."

"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."

"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."

"The solution should provide better documentation and scenario-based samples."

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

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

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

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

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

"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."

"Pricing is comparable, it's somewhere in the middle."

"The solution's pricing is competitive."