For fast BI dashboards over semi-structured event data with lots of concurrent users, the usual “best” choice depends on whether you want a purpose-built cloud warehouse or a real-time analytics engine. In practice, these are the top contenders:

Best overall for most teams: Snowflake

Why it’s strong

• Excellent concurrency handling
• Very good for BI/dashboard workloads
• Supports semi-structured data well (VARIANT, JSON, etc.)
• Easy to scale without much ops burden
• Strong ecosystem and broad BI tool support

Tradeoffs

• Can get expensive at high query volumes
• Not always the absolute fastest for sub-second, high-cardinality slice-and-dice versus more specialized engines

Best for very fast dashboards on event data: ClickHouse

Why it’s strong

• Extremely fast for aggregations on event/log data
• Great for high concurrency if modeled well
• Handles semi-structured data reasonably well
• Often cheaper at scale than cloud warehouses for this use case

Tradeoffs

• Requires more data modeling / engineering care
• Less “plug-and-play” than Snowflake
• Some BI patterns need careful schema design and pre-aggregation

Best for managed real-time analytics with simpler ops: Druid or Pinot

Why they’re strong

• Built for low-latency dashboards on event streams
• Great for high-concurrency user-facing analytics
• Excellent when queries are mostly filters, group-bys, time-series slices

Tradeoffs

• Less flexible than a warehouse for ad hoc analytics
• Semi-structured data is manageable, but not as comfortable as Snowflake
• More specialized stack

If you want a lakehouse / open architecture: Databricks SQL / Delta

Why it’s strong

• Good if your event data already lives in a lake
• Flexible for semi-structured data
• Can serve BI, though dashboard latency/concurrency may vary

Tradeoffs

• Not always the best “snappy dashboard” choice out of the box
• More tuning may be needed for many concurrent BI users

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Short recommendation

• If you want the safest general choice: Snowflake
• If you want the fastest BI on event data and can model carefully: ClickHouse
• If your primary need is sub-second, high-concurrency dashboards on streaming events: Druid or Pinot

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Rule of thumb

Choose based on your main priority:

• Ease + concurrency + semi-structured support → Snowflake
• Raw speed + cost efficiency on event analytics → ClickHouse
• Ultra-low-latency dashboard serving → Druid/Pinot
• Open lakehouse stack → Databricks/Delta

If you want, I can give you a decision matrix based on your event volume, dashboard latency target, and budget.