[{"data":1,"prerenderedAt":101},["ShallowReactive",2],{"case-study:\u002Fcase-studies\u002Fanalytics-api-caching":3},{"id":4,"title":5,"body":6,"date":90,"description":91,"extension":92,"meta":93,"navigation":94,"path":95,"provenance":96,"sensitivityReviewed":90,"seo":97,"sources":98,"stem":99,"__hash__":100},"caseStudies\u002Fcase-studies\u002Fanalytics-api-caching.md","Caching Expensive Analytics Queries",{"type":7,"value":8,"toc":79},"minimark",[9,14,18,22,25,29,32,36,39,43,46,50,72,76],[10,11,13],"h2",{"id":12},"problem","Problem",[15,16,17],"p",{},"The analytics platform at Heartland Payment Systems exposed API endpoints backed\nby expensive Snowflake queries. Some endpoints were requested repeatedly and\nrecomputed the same expensive results each time, creating unnecessary latency\nfor users and unnecessary compute cost on the data warehouse.",[10,19,21],{"id":20},"constraints","Constraints",[15,23,24],{},"The work sat inside a PCI DSS-regulated environment, so any change had to respect\nthat environment's security and operational requirements. Just as importantly,\nthe endpoints already had consumers, so the caching had to be added without\nchanging the consumer-facing API contract, and cached data still had to stay\nappropriately fresh.",[10,26,28],{"id":27},"architecture","Architecture",[15,30,31],{},"I designed and implemented a Redis caching strategy for the frequently requested\nanalytics endpoints. I identified the endpoints where repeated Snowflake queries\ncreated the most avoidable latency and compute, then added Redis caching around\nthat data while preserving appropriate freshness behavior. The implementation\nwas structured to reduce duplicate database work without changing the interface\nits consumers depended on. I used application monitoring and Azure-hosted logs to\nvalidate the behavior and to troubleshoot production issues.",[10,33,35],{"id":34},"tradeoffs","Tradeoffs",[15,37,38],{},"Caching always trades freshness against speed and adds a cache layer to reason\nabout. I kept that trade deliberate and narrow: cache the specific\nhigh-cost, high-repetition endpoints, preserve freshness where it mattered, and\nleave the API contract untouched so no consumer had to change.",[10,40,42],{"id":41},"testing","Testing",[15,44,45],{},"I validated behavior against production signals (application monitoring and\nAzure-hosted logs) to confirm the cache was serving correctly and to\ntroubleshoot issues as they surfaced, rather than assuming the cache behaved as\ndesigned.",[10,47,49],{"id":48},"results","Results",[51,52,53,62,67],"ul",{},[54,55,56,57,61],"li",{},"Reduced response latency from more than two seconds to roughly 40 milliseconds for targeted requests ",[58,59,60],"span",{},"docs\u002Fprofile\u002Fprojects.md",".",[54,63,64,65,61],{},"Lowered the number of repeated Snowflake queries ",[58,66,60],{},[54,68,69,70,61],{},"Improved dashboard responsiveness without sacrificing data freshness ",[58,71,60],{},[10,73,75],{"id":74},"what-id-change-and-what-i-learned","What I'd Change and What I Learned",[15,77,78],{},"Caching in front of an expensive data source is high-leverage, but only when the\nfreshness contract is explicit. The lesson I carry forward is to write down the\nfreshness expectation per endpoint before caching it, so the trade between speed\nand staleness is a stated decision rather than an emergent one.",{"title":80,"searchDepth":81,"depth":81,"links":82},"",2,[83,84,85,86,87,88,89],{"id":12,"depth":81,"text":13},{"id":20,"depth":81,"text":21},{"id":27,"depth":81,"text":28},{"id":34,"depth":81,"text":35},{"id":41,"depth":81,"text":42},{"id":48,"depth":81,"text":49},{"id":74,"depth":81,"text":75},"2026-07-22","A Redis caching strategy for analytics endpoints backed by expensive Snowflake queries, cutting targeted latency from seconds to milliseconds without changing the API contract.","md",{},true,"\u002Fcase-studies\u002Fanalytics-api-caching","professional",{"title":5,"description":91},[60],"case-studies\u002Fanalytics-api-caching","GyROtwr2lQ3mgSWgyUhcTha8fCOICuCGgUtshSb6JMw",1788403975817]