Author name: Abhay

Yesterday you learned how clustering keys and materialized views optimize the data layer — reorganizing micro-partitions for better pruning and pre-computing aggregations for instant reads. Today we’re moving from data-level optimization to compute-level strategy. Here’s a scenario the exam loves: your BI dashboards freeze every morning at 8 AM, right when the overnight ETL batch […]

Yesterday you learned two serverless services that speed up queries at the compute level — QAS for outlier scans, SOS for point lookups. Today we’re going one layer deeper: optimizing how the data itself is physically organized. Clustering keys rearrange data inside micro-partitions so Snowflake can prune more aggressively — but they come with ongoing

Yesterday you mastered Snowflake’s three caching layers — result cache for $0 reruns, metadata cache for instant COUNT(*), and warehouse cache for warm SSD reads. Today we’re covering two serverless services that speed up queries without changing your SQL or resizing your warehouse: the Query Acceleration Service (QAS) and the Search Optimization Service (SOS). They

Yesterday you learned where to find Snowflake’s monitoring data — ACCOUNT_USAGE for 365-day historical depth, INFORMATION_SCHEMA for real-time lookups. Today we’re covering a topic that directly impacts both performance and cost: Snowflake’s three-layer caching system. A query that costs $0 in compute and returns in milliseconds? That’s the result cache. A COUNT(*) that works even

Yesterday you learned how to read the Query Profile — the visual X-ray that shows exactly where a query spent its time and resources. But what happens when you need to look at performance trends across hundreds of queries? Or figure out who’s burning the most credits? Or track login attempts from 6 months ago?