I’m architecting a new dashboard solution in SSRS 2016 and evaluating whether to use shared datasets or direct database connections for our report suite. We have about 40 dashboard reports that will be deployed across different departments, many using similar queries with slight variations. Shared datasets offer centralized query management and seem easier to maintain, but I’m concerned about performance implications and whether caching works effectively. Direct connections give more control but could become a maintenance nightmare. The data access control requirements are also complex - different user groups need filtered views of the same underlying data. What has your experience been with these approaches at scale? Interested in hearing about both performance optimization through caching and the operational reality of maintaining either approach.
I’ve seen performance issues with shared datasets when they’re not designed properly. If your shared dataset pulls too much data and relies on report-level filtering, you’re wasting resources. Direct connections let you write targeted queries that pull exactly what each report needs. However, shared datasets win for frequently accessed data - the caching is powerful if configured correctly. Consider a hybrid approach: shared datasets for common, cacheable data like reference tables and dimensional data, direct connections for transaction-level reporting that needs real-time data. Data access control is actually easier with shared datasets because you can embed row-level security logic once in the dataset query.
Thanks everyone for the insights. The hybrid approach makes a lot of sense - use shared datasets for the common dimensional and reference data that powers most dashboard filters and KPIs, then use direct connections for the detail-level transactional reporting where we need flexibility. The maintenance and security benefits of shared datasets are compelling for our scenario. I’m planning to create about 8-10 well-designed shared datasets that cover our core business entities with proper parameterization, then use those as the foundation for the dashboard suite.
The performance optimization story is nuanced. Shared dataset caching works well for reference data and aggregated metrics that don’t change frequently. Configure cache expiration based on data refresh schedules - if your ETL runs nightly, set cache to expire after 24 hours. For operational dashboards needing near-real-time data, caching becomes less useful and direct connections might perform better because you avoid the overhead of the shared dataset layer. Also consider that shared datasets add one more hop in the execution path. Test both approaches with realistic data volumes and user loads. We found that for datasets under 10,000 rows, the difference is negligible, but for larger result sets, carefully tuned direct queries sometimes outperform shared datasets.