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Peracchio"}},{"authorImage":{"id":"image-813624a0c9","height":"800","src":"/adobe/dynamicmedia/deliver/dm-aid--76e2d637-4ec4-49cc-9679-a556ebb46422/dikshant-adhikari.jpg?preferwebp=true&quality=85","alt":"Headshot style photo of Dikshant Adhikari of Snowflake","lazyEnabled":true,"isLcpImage":false,"width":"800",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-d244511fb3","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/dikshant-adhikari/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Dikshant Adhikari"}}],"image":{"id":"image-7a0607c676","height":"687","src":"/adobe/dynamicmedia/deliver/dm-aid--13cdb7f3-beeb-45e4-9ce0-f2bd17d2df79/ht-featuredimage.jpg?preferwebp=true&quality=85","lazyEnabled":true,"isLcpImage":false,"width":"1200",":type":"snowflake-site/components/image"},"timeToRead":"9","publicationDate":"NOV 10, 2026","tag":{"tagText":"Prodotto e tecnologia","tagColor":"#3F808A"},"title":{"lines":["Hybrid Tables ora fino a 8 volte più veloci, automaticamente"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-636acbcc33",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"button":{"id":"button-2011a22df8","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"UNKNOWN"}},":itemsOrder":["blog_hero","container","button"]},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"code_snippet_1852616005":"aem-GridColumn aem-GridColumn--default--12","blog_component_27":"aem-GridColumn aem-GridColumn--default--12","blog_component_26":"aem-GridColumn aem-GridColumn--default--12","blog_text_1304711887":"aem-GridColumn 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aem-GridColumn--default--12","blog_text_568847750":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_component_0":{"id":"blog-text-98fd2b36d6","text":"\u003Cp\u003EA giugno abbiamo \u003Ca href=\"https://www.snowflake.com/it/blog/hybrid-tables-performance-improvements/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Epresentato in preview una nuova generazione di performance per le Snowflake Hybrid Tables\u003C/a\u003E. \u003Cstrong\u003EOggi questi miglioramenti sono in GA e attivi per impostazione predefinita.\u003C/strong\u003E\u003C/p\u003E\u003Cp\u003ELe \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHybrid Tables\u003C/a\u003E ora \u003Cstrong\u003Eoffrono throughput fino a 8 volte superiore, latenza p50 3 volte inferiore e latenza p99 2 volte inferiore.\u003C/strong\u003E¹ Per le migliaia di clienti che già utilizzano le Hybrid Tables, i workload esistenti diventeranno automaticamente più rapidi ed economici, con margine per gestire volumi maggiori sulla stessa capacità di compute.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_1":{"id":"blog-title-180c072cd0","propertiesId":"dati-transazionali-senza-un-database-separato","type":"heading2","lines":["Dati transazionali senza un database separato"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_2":{"id":"blog-text-09d8afabe2","text":"\u003Cp\u003EPipeline, agenti AI e app interne dipendono tutti da letture e scritture transazionali rapide, sia per tracciare lo stato dei job, sia per memorizzare il contesto degli agenti o registrare le azioni degli utenti nelle app enterprise realizzate con \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStreamlit\u003C/a\u003E o \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime/about-snowflake-app-runtime\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake App Runtime\u003C/a\u003E. Troppo spesso questo ha significato mettere in piedi un database operativo separato, una pipeline per mantenerlo sincronizzato con l’analisi dei dati e un secondo set di controlli di governance.\u003C/p\u003E\u003Cp\u003ELe Hybrid Tables portano invece letture e scritture rapide e conformi ad ACID direttamente nel database Snowflake. Dati transazionali e analitici convivono sotto lo stesso framework di governance e si possono unire in join in un’unica query. E con questa release, app e workflow esistenti ottengono un miglior rapporto prezzo/prestazioni senza nulla da riprogettare.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"blog-title-ddcbb72c8c","propertiesId":"cosa-e-in-ga-oggi-per-le-hybrid-tables","type":"heading2","lines":["Cosa è in GA oggi per le Hybrid Tables"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_4":{"id":"blog-title-973b92c002","propertiesId":"throughput-fino-a-8-volte-superiore","type":"heading3","lines":["Throughput fino a 8 volte superiore"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_5":{"id":"blog-text-98ac2b465c","text":"\u003Cp\u003EL’esecuzione di query a singolo statement per operazioni puntuali ripetitive offre ora un throughput fino a 8 volte superiore.¹ Significa che, per alcuni workload, ciò che prima richiedeva otto warehouse XS ora ne richiede uno solo, con una riduzione dei costi di compute superiore all’80%.\u003Csup\u003E2\u003C/sup\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_6":{"id":"image-6e7892ca46","height":"597","src":"/adobe/dynamicmedia/deliver/dm-aid--25b59bff-0df1-4460-a93d-860fa2367adf/hybrid-tables-8x-performance-chart.png?preferwebp=true&quality=85","alt":"Bar chart showing 8x performance improvement from Standard Tables to Hybrid Tables with new engine","lazyEnabled":true,"isLcpImage":true,"width":"1112","title":"Figure 1: Hybrid Tables 2025 compared to the new Hybrid Tables engine. Data is based on benchmarks run using the Yahoo Cloud Serving Benchmark (YCSB) with a 100% read workload on a Gen2 XS warehouse.",":type":"snowflake-site/components/image"},"blog_component_7":{"id":"blog-title-5487169bf7","propertiesId":"latenza-inferiore-sulla-mediana-e-sulla-coda","type":"heading3","lines":["Latenza inferiore sulla mediana e sulla coda"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_8":{"id":"blog-text-6ceafa9bb1","text":"\u003Cp\u003E\u003Cb\u003ECon il nuovo engine delle Hybrid Tables,\u003C/b\u003E i risultati dei benchmark mostrano che \u003Cb\u003Ela latenza p50 è 3 volte inferiore e la latenza p99 2 volte inferiore.\u003C/b\u003E\u003Csup\u003E3\u003C/sup\u003E Una mediana più rapida significa risposte più veloci su ogni richiesta. Una coda più contenuta significa meno richieste lente nei picchi di carico, ed è proprio ciò che utenti e sistemi a valle percepiscono di più.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_9":{"id":"blog-title-8055443733","propertiesId":"bulk-load-piu-rapidi-ed-economici","type":"heading3","lines":["Bulk load più rapidi ed economici"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_10":{"id":"blog-text-17ba3fdca6","text":"\u003Cp\u003EGli insert bulk sono ottimizzati a livello di storage engine e i risultati dei benchmark dimostrano \u003Cb\u003Efino a\u003C/b\u003E \u003Cb\u003Escritture batch 10 volte più rapide con costi 10 volte inferiori,\u003C/b\u003E anche su tabelle con dati già presenti.\u003Csup\u003E3\u003C/sup\u003E I backfill e le sincronizzazioni di grandi volumi che un tempo richiedevano un’orchestrazione accurata ora funzionano con un semplice INSERT.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_11":{"id":"blog-title-8c2a34992d","propertiesId":"pricing-piu-semplice","type":"heading3","lines":["Pricing più semplice"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_12":{"id":"blog-text-6b63ce14f3","text":"\u003Cp\u003ELe Hybrid Tables seguono lo stesso modello basato su compute e storage che i clienti Snowflake già conoscono, ma abbiamo eliminato una delle dimensioni, così la fatturazione si basa su meno voci. \u003Cb\u003EOggi i clienti registrano una riduzione dei costi del 15% in media e del 40% o più per i workload a throughput elevato.\u003C/b\u003E\u003Csup\u003E4\u003C/sup\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003E\u003Cb\u003EEvoluzione delle Hybrid Tables\u003C/b\u003E\u003C/th\u003E\n\u003Cth\u003E\u003Cb\u003EI risultati\u003C/b\u003E\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EThroughput più elevato\u003C/td\u003E\n\u003Ctd\u003EFino a 8 volte più throughput, applicato automaticamente\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPrestazioni batch più rapide\u003C/td\u003E\n\u003Ctd\u003EBulk load fino a 10 volte più rapidi e 10 volte più economici, senza modifiche ai workflow\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ERiduzione dei costi\u003C/td\u003E\n\u003Ctd\u003ERisparmi sui costi del 40% o più\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_13":{"id":"blog-title-de5aeb4184","propertiesId":"dove-i-clienti-utilizzano-le-hybrid-tables","type":"heading2","lines":["Dove i clienti utilizzano le Hybrid Tables"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_14":{"id":"blog-text-9a966df48f","text":"\u003Cp\u003EChe si tratti di sviluppare un’app interna, eseguire una pipeline o orchestrare agenti AI, i workload su Hybrid Tables seguono in genere i tre pattern descritti di seguito. Molte app ne combinano più di uno.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_15":{"id":"blog-title-8497140b82","propertiesId":"stato-dei-workflow","type":"heading3","lines":["Stato dei workflow"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_16":{"id":"blog-text-082331506b","text":"\u003Cp\u003EPipeline, app e agenti hanno tutti bisogno di un posto in cui tracciare lo stato: stato dei job, checkpoint, sessioni e risultati intermedi. Spesso i team hanno utilizzato un database esterno solo per questo. Le Hybrid Tables mantengono lo stato dei workflow coerente a livello transazionale, nello stesso database Snowflake in cui risiedono i dati analitici prodotti da quei workflow, così i team possono interrogarli insieme.\u003C/p\u003E\u003Cp\u003EPer esempio, \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/marketwise/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EMarketWise\u003C/a\u003E ha sostituito MySQL e DynamoDB per tracciare in tempo reale lo stato dei workflow delle sue pipeline di data engineering, \u003Cstrong\u003Eriducendo i costi di infrastruttura del 35% ed eliminando movimenti di dati non necessari.\u003C/strong\u003E \u003Ca href=\"https://medium.com/snowflake/how-elementum-builds-the-next-generation-of-ai-driven-workflows-with-unistores-hybrid-tables-1f2cf4f9d67c\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EElementum\u003C/a\u003E orchestra agenti AI, regole di business e passaggi umani in processi multi-step sulle Hybrid Tables, con vincoli di primary key e foreign key applicati.\u003C/p\u003E\u003Cp\u003EIn preview, i workload di stato dei workflow dei clienti sono risultati fino a 5 volte più rapidi a p50 e 2,8 volte più rapidi a p99. Un cliente ha visto la latenza p90 scendere da 21 a 5 millisecondi, eseguendo il 55% di job in più.⁵\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_17":{"id":"blog-title-4508b9610d","propertiesId":"serving-dei-dati-applicativi","type":"heading3","lines":["Serving dei dati applicativi"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_18":{"id":"blog-text-5baa8e410f","text":"\u003Cp\u003EQuando un’app o un agente deve rispondere immediatamente a una domanda, per esempio se approvare un reso, quale prodotto consigliare o in che stato si trova una macchina, le Hybrid Tables trasformano Snowflake nel serving tier. I workload di data serving sono quelli che traggono i maggiori vantaggi da questa release. In preview hanno registrato latenza p50 fino a 6,7 volte inferiore e latenza p99 fino a 6,3 volte inferiore.⁵ Ora i team gestiscono molte più lookup concorrenti con molto meno compute.\u003C/p\u003E\u003Cp\u003EPer esempio, \u003Ca href=\"https://medium.com/snowflake/grailed-revolutionizes-style-discovery-with-snowflake-unistore-e7c5061e5795\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EGrailed\u003C/a\u003E fornisce a milioni di utenti raccomandazioni di stile personalizzate in meno di un secondo, a partire da risultati ML precalcolati nelle Hybrid Tables e senza un database di serving separato.\u003C/p\u003E\u003Cp\u003ELo stesso pattern si adatta ad ambienti operativi esigenti come il manifatturiero, la logistica e la supply chain. Gli operatori hanno bisogno di lookup in millisecondi sullo stato attuale di un job, di una macchina o di una spedizione, mentre chi si occupa di pianificazione esegue analisi sugli stessi dati. Le Hybrid Tables servono le lookup e le tabelle standard gestiscono i join su larga scala, entrambe su una sola piattaforma e senza tuning tabella per tabella. È qui che i miglioramenti su p99 contano di più: una coda lenta può bloccare una linea di produzione o una pianificazione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_19":{"id":"blog-title-966f42655e","propertiesId":"scritture-affidabili-ad-alta-concorrenza","type":"heading3","lines":["Scritture affidabili ad alta concorrenza"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_20":{"id":"blog-text-b8a9ac4d7c","text":"\u003Cp\u003EAlcuni workload scrivono costantemente. App e servizi registrano ogni azione utente, chiamata API, invocazione di modello o transazione come record che deve essere scritto in modo affidabile. Le tabelle standard sono progettate per l’analisi dei dati e possono incorrere in contese di lock con scritture concorrenti intense. Le Hybrid Tables usano un locking a livello di riga progettato esattamente per questo pattern.\u003C/p\u003E\n\u003Cp\u003EIn preview, i workload di event log e record capture sono risultati fino a 3,4 volte più rapidi a p50 e 3,7 volte più rapidi a p99. In un cliente, il workload di log operativi ha visto la latenza di insert p90 ridursi del 66%, gestendo oltre 3 volte il volume di job.⁵\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://medium.com/snowflake/how-writer-keeps-its-ai-infrastructure-running-with-snowflake-hybrid-tables-80f55dd652fa\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EWriter\u003C/a\u003E, piattaforma AI enterprise, scrive ogni giorno circa 1,3 milioni di record di trace LLM su Snowflake per alimentare observability e QA dei modelli. Su una tabella standard, la latenza di insert p50 è schizzata e fino al 45% delle scritture non andava a buon fine. \u003Cb\u003EWriter ha cambiato la destinazione passando a una Hybrid Table, in pratica uno scambio di tabella, e la latenza di insert p50 è scesa di circa 15 volte. Gli errori si sono ridotti quasi a zero.\u003C/b\u003E Le Hybrid Tables assorbono le scritture, mentre una tabella standard clusterizzata per timestamp alimenta le analisi, tutto nello stesso database.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_21":{"id":"image-8673f1c8aa","height":"441","src":"/adobe/dynamicmedia/deliver/dm-aid--e5e5dc1f-d4af-47ff-b7f8-11cfd88835d4/line-chart-showing-error-count-trends-over-time-from-june.png?preferwebp=true&quality=85","alt":"Line chart showing error count trends over time from June to July with peaks reaching 500,000 and a red arrow highlighting early July","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 2: INSERT errors before and after Writer’s cutover to a Hybrid Table, with failures falling to near zero.",":type":"snowflake-site/components/image"},"blog_component_22":{"id":"blog-title-18dbe5ff58","propertiesId":"appriss-retail-blocca-le-frodi-con-dati-sempre-aggiornati","type":"heading3","lines":["Appriss Retail blocca le frodi con dati sempre aggiornati"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_23":{"id":"blog-text-e6e76ddcb2","text":"\u003Cp\u003E\u003Ca href=\"https://apprissretail.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EAppriss Retail aiuta a bloccare le frodi\u003C/a\u003E legate a resi e reclami per oltre 60 dei primi 100 retailer statunitensi, con un’accuratezza del 99,99%. Ogni reso e ogni reclamo riceve un punteggio in tempo reale. Acquisti, resi e reclami di ogni cliente sono riuniti in un unico profilo. I segnali di rischio sono calcolati da quello storico nel momento del reso, per approvare, segnalare o negare.\u003C/p\u003E\u003Cp\u003EPrima delle Hybrid Tables, questo scoring dipendeva da un database SQL Server separato, alimentato da una pipeline ETL personalizzata. Il tempo dei team di engineering andava nella manutenzione della pipeline invece che nel miglioramento del rilevamento. Spostare i dati tra sistemi significava decidere sulle frodi basandosi su dati non aggiornati. E il caricamento dei dati rallentava lo stesso database che gestiva resi e reclami live.\u003C/p\u003E\u003Cp\u003EOra le app di resi e reclami dei retailer eseguono le lookup direttamente sulle Hybrid Tables, mentre i team di data science interrogano le tabelle standard sulla stessa piattaforma. Le lookup si completano rapidamente e le analisi scansionano terabyte di dati, tutto in Snowflake. La pipeline personalizzata non c’è più e le decisioni si basano sui dati nell’istante in cui sono generati.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_24":{"id":"image-b75011406c","height":"486","src":"/adobe/dynamicmedia/deliver/dm-aid--48628ede-3159-43a2-9916-3de0da7543f3/architecture-diagram-comparing-before-and-after-data.jpg?preferwebp=true&quality=85","alt":"Architecture diagram comparing before and after data pipeline designs, showing migration from SQL Server to Snowflake","lazyEnabled":true,"isLcpImage":false,"width":"1092","title":"Figure 3: Appriss Retail’s new workflow with Hybrid Tables reduced lookup latency, providing data science teams with timely data for their work.",":type":"snowflake-site/components/image"},"quote_item":{"id":"quote-item-dd0248dc0d","alignment":"left","layout":"card","quoteSourceName":"Kenny Vu","quoteSourceTitle":"VP of Engineering, Appriss Retail","showQuoteIcon":false,"quote":"“Ogni reso e ogni reclamo gestito dal nostro retailer più grande viene ora verificato tramite Hybrid Tables. Parliamo di oltre un milione di ricerche al giorno, senza esportare alcun dato da Snowflake.”","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_26":{"id":"blog-title-3900ba4148","propertiesId":"come-iniziare-con-le-hybrid-tables","type":"heading2","lines":["Come iniziare con le Hybrid Tables"],":type":"snowflake-site/components/blog/blog-title"},"blog_text":{"id":"blog-text-a45fd181e3","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_27":{"id":"blog-text-015182c2d6","text":"\u003Cp\u003ESe utilizzi già le Hybrid Tables, non devi fare nulla. I miglioramenti sono attivi per impostazione predefinita.\u003C/p\u003E\n\u003Cp\u003ENon hai mai usato le Hybrid Tables? Per crearne una basta un singolo statement e puoi unirla subito in join con le tue tabelle esistenti.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ECrea una Hybrid Table per tracciare lo stato dei job di una pipeline\u003C/b\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-3117ca4580","codeSnippet":"CREATE HYBRID TABLE job_state (\r\n  job_id      INT PRIMARY KEY,\r\n  status      VARCHAR,\r\n  updated_at  TIMESTAMP_NTZ,\r\n  INDEX idx_status (status)\r\n);\r\n","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1178314163":{"id":"blog-text-94826592f4","text":"\u003Cp\u003E\u003Cb\u003EScritture e lookup puntuali rapide\u003C/b\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1418335275":{"id":"code-snippet-6c402fd4cf","codeSnippet":"INSERT INTO job_state VALUES (1001, 'RUNNING', CURRENT_TIMESTAMP());\r\nSELECT status FROM job_state WHERE job_id = 1001;\r\n","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1304711887":{"id":"blog-text-6ac2b1057d","text":"\u003Cp\u003E\u003Cb\u003EUnisci lo stato transazionale ai dati analitici in un’unica query\u003C/b\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1852616005":{"id":"code-snippet-41ed975e2e","codeSnippet":"SELECT j.job_id, j.status, r.rows_processed\r\nFROM job_state j\r\nJOIN job_run_metrics r ON j.job_id = r.job_id\r\nWHERE j.status = 'FAILED';\r\n","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_568847750":{"id":"blog-text-5a6ce03ff5","text":"\u003Cp\u003EDa qui puoi seguire un esempio completo con la \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tutorials/getting-started-with-hybrid-tables-tutorial\" target=\"_blank\"\u003Equickstart guide\u003C/a\u003E ed esplorare la documentazione sulle \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-hybrid-best-practices\" target=\"_blank\"\u003Ebest practice per le Hybrid Tables.\u003C/a\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E¹ Dati basati su benchmark eseguiti con lo Yahoo Cloud Serving Benchmark, con workload di lettura al 100% su un warehouse XS Gen2. \u003C/p\u003E\n\u003Cp\u003E\u003Csup\u003E2\u003C/sup\u003E Derivato da un throughput 8 volte superiore su un singolo warehouse XS rispetto a otto warehouse XS a carico equivalente.\u003C/p\u003E\n\u003Cp\u003E\u003Csup\u003E3\u003C/sup\u003E Dati basati su benchmark interni per il caricamento di 50 GB nelle Hybrid Tables con un warehouse 2XL.\u003C/p\u003E\n\u003Cp\u003E\u003Csup\u003E4\u003C/sup\u003E Dati basati su stime Snowflake del risparmio medio sui costi tra i clienti, misurato sul consumo reale di credit in produzione a febbraio 2026.\u003C/p\u003E\n\u003Cp\u003E⁵ Dati basati su osservazioni Snowflake della latenza di query p50, p90 e p99 per workload di clienti in produzione, prima e dopo l’attivazione del nuovo engine delle Hybrid Tables durante la public preview.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_1","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_component_8","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17","blog_component_18","blog_component_19","blog_component_20","blog_component_21","blog_component_22","blog_component_23","blog_component_24","quote_item","blog_component_26","blog_text","blog_component_27","code_snippet","blog_text_1178314163","code_snippet_1418335275","blog_text_1304711887","code_snippet_1852616005","blog_text_568847750"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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transazionali senza un database separato","level":"h2","anchorId":"#dati-transazionali-senza-un-database-separato","hierarchicalChildrenStructure":[]},{"headingText":"Cosa è in GA oggi per le Hybrid Tables","level":"h2","anchorId":"#cosa-e-in-ga-oggi-per-le-hybrid-tables","hierarchicalChildrenStructure":[{"headingText":"Throughput fino a 8 volte superiore","level":"h3","anchorId":"#throughput-fino-a-8-volte-superiore","hierarchicalChildrenStructure":[]},{"headingText":"Latenza inferiore sulla mediana e sulla coda","level":"h3","anchorId":"#latenza-inferiore-sulla-mediana-e-sulla-coda","hierarchicalChildrenStructure":[]},{"headingText":"Bulk load più rapidi ed economici","level":"h3","anchorId":"#bulk-load-piu-rapidi-ed-economici","hierarchicalChildrenStructure":[]},{"headingText":"Pricing più semplice","level":"h3","anchorId":"#pricing-piu-semplice","hierarchicalChildrenStructure":[]}]},{"headingText":"Dove i clienti utilizzano le Hybrid 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