The original title is "Problem Framing" which is very generic and not a news headline. I need to rewrite it based on the summary content.

Short summary

Slow real-time analytics dashboards are often caused by high-cardinality joins that force massive data shuffles across distributed clusters, not by raw query engine performance. The solution is to denormalize low-cardinality attributes from dimension tables into fact tables at ingestion time using stream processing pipelines like Kafka and Flink. This shifts computational cost from query execution to the ingestion layer, eliminating expensive joins and reducing query latency dramatically.

• •High-cardinality joins cause network-heavy data shuffles in distributed analytics databases, killing query performance
• •Denormalize low-cardinality dimension attributes into fact tables at ingestion time via stream processing to avoid query-time joins
• •Uber and similar scale companies use Kafka, Flink, and Spark to enrich events before they land in analytical stores like ClickHouse

Generated with AI, which can make mistakes.