Design a Server-Side Analytics Dashboard

To design a server-side analytics dashboard for Salesforce, I would start by clarifying that we need to process billions of daily events from CRM interactions while ensuring sub-second query times for internal stakeholders. First, I would implement an asynchronous ingestion pipeline using Kafka to decouple data collection from processing, ensuring system resilience during traffic spikes. For storage, I would reject traditional row-based SQL databases in favor of a columnar OLAP database like Apache Druid or a dedicated Cube instance. These systems are optimized for heavy aggregations on large datasets, allowing us to compute complex metrics like 'conversion rates per region' efficiently without scanning entire tables. Next, I would design a microservice architecture where a background worker processes raw logs into aggregated cubes stored in the OLAP engine. The frontend would query these pre-aggregated results via a GraphQL API, which handles dynamic filtering and slicing. To ensure scalability, I would partition data by tenant ID and time range, enabling horizontal scaling. Finally, I would add a Redis cache layer for common dashboard views to further reduce load on the OLAP engine, ensuring consistent performance even as data grows exponentially.