Azure Data Factory vs TIBCO Live Datamart comparison

"We have been using drivers to connect to various data sets and consume data."

"Azure Data Factory is a low code, no code platform, which is helpful."

"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."

"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."

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

"The solution has a good interface and the integration with GitHub is very useful."

"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 aspect is the copy capability."

"You can create your own rules that include mathematic calculations."

"The solution has a powerful aggregating feature"

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

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

"I have encountered a problem with the integration with third-party solutions, particularly with SAP."

"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."

"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."

"There are limitations when processing more than one GD file."

"I would like to be informed about the changes ahead of time, so we are aware of what's coming."

"The one element of the solution that we have used and could be improved is the user interface."

"Improvements need to be made on the load balancing side."

"The solution's setup could be quicker and easier."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"Data Factory is affordable."

"The licensing cost is included in the Synapse."

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

"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."

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

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

"The solution might be considered a bit expensive because it competes with open-source products."