I'm unhappy with our current analytics database not handling concurrency

If your analytics database is struggling with concurrency, the fix is usually less about “tuning one knob” and more about matching the workload to the right architecture.

A few common paths:

1. Separate OLTP from analytics

   • Keep heavy transactional writes out of the analytics system.
   • Use CDC/streaming/ETL to feed analytics from the source system asynchronously.

2. Use an analytics engine designed for high concurrency

   • Columnar MPP systems often handle many concurrent reads better than row-oriented databases.
   • Examples: BigQuery, Snowflake, Redshift, ClickHouse, Druid, Pinot, etc., depending on workload.

3. Add workload isolation

   • Use separate clusters/warehouses for BI users, ad hoc queries, and scheduled jobs.
   • This prevents one expensive dashboard or report from starving everyone else.

4. Optimize query patterns

   • Pre-aggregate common metrics.
   • Materialized views / summary tables.
   • Partitioning and clustering on the right dimensions.
   • Limit expensive joins at query time.

5. Control concurrency explicitly

   • Query queues, resource groups, or workload managers.
   • Concurrency limits per user/team.
   • Timeouts and cost guards.

6. Scale out read capacity

   • If the database supports it, add read replicas or separate compute from storage.
   • Some systems scale concurrency much better by decoupling storage/compute.

If you want, I can help you pick a better design if you tell me:

• what database you’re using now,
• approximate query volume / concurrent users,
• read vs write ratio,
• and the main pain: slow queries, lock contention, or users blocking each other.