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AI/BI-powered Analytics have 3 assumptions: [1] dimensional data model [2] transformation pipeline [3] data integrity & quality. However, the realistic gaps still exist in all these areas which lead to lower-than-expected accuracy of AI-for-analytics. This article explains the WHY and HOW to `shift left` and mitigate this from the ground truth level by investing into the true data engineering paradiams.
Understand the pitfall of I/O saturation symptom caused by insanely simple queries in ClickHouse and StarRocks. Learn the best practice of physical data modeling and query shape for decent QPS analytical workloads.
Understand the 'heatmap' of Snowflake costs and how to focus on the most impactful areas (compute/warehouse allocation, highly-skewed workload, SQL craftsmanship, migration to other engines, operational intelligence) with effective mitigations.
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