Best Practices for Optimizing Sisense Dashboards for Large Datasets

Hi everyone,

I’m currently working on optimizing some Sisense dashboards that are handling large datasets, and I’d love to get some insights from the community on best practices.

We’re pulling in millions of records, and while performance is decent, I’d like to make it even more efficient. Here are a few specific challenges I’m facing:

1. Slow Load Times: Some dashboards take a while to load, especially when filters are applied. Are there specific design techniques or settings that can help speed this up?
2. Aggregation Strategy: Would it be more efficient to pre-aggregate certain calculations in the Elasticube rather than doing them in the dashboard? If so, how do you determine which ones to pre-aggregate?
3. Materialized Views vs. Elasticube Optimization: Have you found materialized views in the source database to be a better approach than optimizing the Elasticube? What’s been your experience with balancing these two strategies?
4. Best Plugins or Extensions: Are there any plugins or third-party tools you’d recommend for performance monitoring or caching that integrate well with Sisense?
5. Data Modeling Tips: What are some best practices for structuring the Elasticube to minimize unnecessary joins and calculations of power apps certification?

I appreciate any advice or real-world experiences you can share! Looking forward to learning from the community.

Thanks in advance!

Regards

Delilah