Which action can help to create better dashboard performance when datasets grow large?

Which action can help to create better dashboard performance when datasets grow large?

• A. Combine all datasets into one
• B. Use filters extensively
• C. Move calculations to a dataflow
• D. Revert to using static visualizations

Answer: (C)

Moving calculations to a dataflow is an effective strategy for improving dashboard performance as datasets grow larger. Dataflows allow you to precompute and store aggregate data or perform transformations before they are consumed by the dashboard. By offloading complex calculations and aggregations into a dataflow, you reduce the workload on the dashboard at runtime, thereby increasing its speed and efficiency. When calculations are handled directly within the dashboard, each interaction can lead to additional processing, especially if the datasets are large. This can result in slower load times and a less responsive user experience. However, when calculations are made in a dataflow, the dashboard can quickly access pre-computed results, leading to improved performance. In contrast, combining all datasets into one may lead to bloated data and can complicate maintenance without necessarily improving performance. Extensive use of filters may help in managing which data is displayed but can still lead to performance issues if the underlying data is overly complex. Reverting to static visualizations eliminates interactivity and does not leverage the insights that can be gained through dynamic analytics, reducing the overall functionality of the dashboard.

Moving calculations to a dataflow is an effective strategy for improving dashboard performance as datasets grow larger. Dataflows allow you to precompute and store aggregate data or perform transformations before they are consumed by the dashboard. By offloading complex calculations and aggregations into a dataflow, you reduce the workload on the dashboard at runtime, thereby increasing its speed and efficiency.

When calculations are handled directly within the dashboard, each interaction can lead to additional processing, especially if the datasets are large. This can result in slower load times and a less responsive user experience. However, when calculations are made in a dataflow, the dashboard can quickly access pre-computed results, leading to improved performance.

In contrast, combining all datasets into one may lead to bloated data and can complicate maintenance without necessarily improving performance. Extensive use of filters may help in managing which data is displayed but can still lead to performance issues if the underlying data is overly complex. Reverting to static visualizations eliminates interactivity and does not leverage the insights that can be gained through dynamic analytics, reducing the overall functionality of the dashboard.