We performed a comparison between Azure Data Factory and TIBCO Spotfire based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."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."
"It makes it easy to collect data from different sources."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."
"The overall performance is quite good."
"I like the basic features like the data-based pipelines."
"Powerful but easy-to-use and intuitive."
"Data discovery is very simple."
"The solution is an affordable one."
"The most valuable features of TIBCO Spotfire are visualization. It's very nice, clear, and, flexible. You can analyze many data types and the user interface is very good."
"Spotfire is excellent for scientific applications, especially because of its integration with RNG."
"R and automation services."
"The product's initial setup phase was simple."
"It's scalable."
"We used it pretty heavily in gathering data, which goes to our VPs. They are able to base their next revenue planning based on this metric. So, it has been invaluable."
"Lacks in-built streaming data processing."
"I have not found any real shortcomings within the product."
"Azure Data Factory can improve by having support in the drivers for change data capture."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."
"There's space for improvement in the development process of the data pipelines."
"The setup and configuration process could be simplified."
"The initial setup of TIBCO Spotfire is in the middle range of difficulty. The initial setup of Power BI is easier."
"The data compression has room for improvement."
"Personalising Spotfire, as a whole, is painful and is something that could be made easier."
"I would like more easy-to-implement analytical algorithms. At the moment they include things like forecasts and regressions. They need to add a lot more of these types of things to the product because not everyone is a data scientist."
"I would like to see the visualization library in this solution expanded, so that I can increase what I can offer to our clients."
"The initial setup is complicated and needs experience and knowledge."
"We would like to see some development in the visualization aspect of this solution, as they can currently only be configured in the cloud, which limits us to one endpoint. It would be helpful if this could be integrated with other tools."
"The Text Area feature needs to be more user-friendly. It is a little clunky to use, and it is difficult to consistently format text."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while TIBCO Spotfire is ranked 13th in BI (Business Intelligence) Tools with 66 reviews. Azure Data Factory is rated 8.0, while TIBCO Spotfire is rated 8.2. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of TIBCO Spotfire writes "A robust BI tool, with good dashboard features and adverse event tracking". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas TIBCO Spotfire is most compared with Tableau, Microsoft Power BI, Qlik Sense, QlikView and Amazon QuickSight. See our Azure Data Factory vs. TIBCO Spotfire report.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
