Enhanced table data configuration in dashboards and low-code apps

We’re very excited to share a new update to our Dashboards and Low-Code Apps modules focusing on streamlining the data configuration and performance for table parameters.

The old configuration: room for improvement

Have you ever struggled with parameter tables when constructing a dashboard, trying to get a component to display specific cells? This could for example be with a line chart component, if you want to show trending for one cell of a parameter table.

Up to now this involved dragging the line chart visualization onto the dashboard and then populating it with a table column from the “Parameters” data set. But then you’d also need to manually locate the row in the “Indices” data set as well, and drag that onto the line chart as a filter.

But if you just want to show the trending for a single metric, this is not all that easy and intuitive. Room for improvement, so we’ve made some changes!

Introducing cell parameters

With the DataMiner 10.3.9 update, you will be able to access cell parameters directly, making it much easier to display and analyze targeted data points

To incorporate a cell parameter in a component, the workflow is now much simpler. Expand the table column data node, and you’ll immediately see the first 100 cells associated with the table column. That way you can just drag the right cell to the component without having to spend extra time and effort on creating a filter.

Optimized performance and efficiency

To ensure the best performance and make sure everything keeps running smoothly, we’ve implemented measures to optimize the way cell parameters are handled. This includes the restriction to only display the first 100 entries, which has been carefully designed to balance performance and usability. But that’s not all!

• Enhanced filtering: We’ve introduced a filter box that you can use to quickly and efficiently narrow down your search and find the right cell.
• Caching for performance: The initial cells for multiple columns will now be cached, preventing unnecessary duplicate calls and reducing the time it takes to fetch data.
• Table-level result caching: We’ve also implemented result caching at the table level. When cells have been fetched, they are now cached so that performance is optimized when they need to be retrieved again.
• Improved performance for visualizations using cell parameters: With the new cell parameters, the performance of visualizations such as the line chart and state component has improved by approximately 20%. We eliminated the need for cross-matching data calls, streamlining the process and expediting the rendering of these components.