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-->\n","\u003Ch2\u003EElaborare dati semi-strutturati in Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EPanoramica\u003C/h3\u003E\n","\u003Cp\u003EQuesto quickstart Powered by Tasty Bytes - Da zero a Snowflake è dedicato all’elaborazione dei dati semi-strutturati.\u003C/p\u003E\n","\u003Cp\u003ENel corso del quickstart impareremo come elaborare i dati semi-strutturati in Snowflake esaminando il tipo di dati VARIANT, come elaborare i dati semi-strutturati con una combinazione di notazione punto e appiattimento laterale e come creare viste e grafici in Snowsight.\u003C/p\u003E\n","\u003Cp\u003EPer maggiori dettagli sui dati semi-strutturati in Snowflake, consulta la \u003Ca href=\"https://docs.snowflake.com/en/user-guide/semistructured-concepts\"\u003Edocumentazione introduttiva sui dati semi-strutturati\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisiti\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPrima di iniziare, assicurati di avere completato il \u003Ca href=\"/it/developers/guides/tasty-bytes-introduction-it/\"\u003E\u003Cstrong\u003Equickstart Introduzione a Tasty Bytes\u003C/strong\u003E\u003C/a\u003E, che spiega nei dettagli come configurare un account di prova e implementare l’ambiente base di Tasty Bytes necessario per completare questo quickstart.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECosa imparerai\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EChe cos’è il tipo di dati VARIANT\u003C/li\u003E\u003Cli\u003ECome utilizzare la notazione punto per interrogare i dati semi-strutturati\u003C/li\u003E\u003Cli\u003ECome appiattire i dati semi-strutturati\u003C/li\u003E\u003Cli\u003ECome analizzare gli array\u003C/li\u003E\u003Cli\u003ECome creare una vista\u003C/li\u003E\u003Cli\u003ECome creare un grafico a partire da un set di risultati in Snowsight\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECosa realizzerai\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUn processo di gestione dei dati semi-strutturati end-to-end\u003C/li\u003E\u003Cli\u003EUna vista armonizzata (Silver) e analitica (Gold)\u003C/li\u003E\u003Cli\u003EUn grafico a barre utile per i dirigenti\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECreare un foglio di lavoro e copiare il codice SQL\u003C/h2\u003E\n","\u003Ch3\u003EPanoramica\u003C/h3\u003E\n","\u003Cp\u003EIn questo quickstart seguiremo la storia del cliente Tasty Bytes attraverso un foglio di lavoro SQL di Snowsight, utilizzando questa pagina come guida completa di commenti aggiuntivi, immagini e link alla documentazione.\u003C/p\u003E\n","\u003Cp\u003EIn questa sezione vedremo nei dettagli come accedere a Snowflake, creare un nuovo foglio di lavoro, rinominarlo, copiare da GitHub il codice SQL che utilizzeremo in questo quickstart e incollarlo nel foglio di lavoro.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 1 - Accedere a Snowflake tramite URL\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EApri una finestra del browser e inserisci l’URL del tuo account Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 2 - Effettuare l’accesso a Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccedi al tuo account Snowflake.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/log_into_snowflake.gif\" alt=\"assets/log_into_snowflake.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 3 - Accedere ai fogli di lavoro\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFai clic sulla scheda Worksheets nella barra di navigazione sulla sinistra.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/worksheet_tab.png\" alt=\"assets/worksheet_tab.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 4 - Creare un foglio di lavoro\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ENella scheda Worksheets, fai clic sul pulsante “+” nell’angolo superiore destro di Snowsight e scegli “SQL Worksheet”\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/+_sqlworksheet.png\" alt=\"assets/+_sqlworksheet.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 5 - Rinominare un foglio di lavoro\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERinomina il foglio di lavoro facendo clic sul nome generato automaticamente (data e ora) e inserendo “Tasty Bytes - Semi-Structured Data”.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/rename_worksheet_tasty_bytes_setup.gif\" alt=\"assets/rename_worksheet_tasty_bytes_setup.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 6 - Accedere al codice SQL di questo quickstart in GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFai clic sul pulsante qui sotto, che ti porterà al file SQL di Tasty Bytes archiviato su GitHub. <button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql\"\u003Etb_zts_semi_structured_data.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 7 - Copiare la configurazione SQL da GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EIn GitHub, vai sul lato destro e fai clic su “Copy raw contents”. Tutto il codice SQL necessario verrà copiato nei tuoi Appunti.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 8 - Incollare la configurazione SQL da GitHub nel tuo foglio di lavoro Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ETorna a Snowsight e al foglio di lavoro che hai appena creato e incolla (\u003Cem\u003ECMD + V per Mac o CTRL + V per Windows\u003C/em\u003E) il codice che abbiamo appena copiato da GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPassaggio 9 - Fai clic su Next -->\u003C/h3\u003E\n","\u003Ch2\u003EProfilare i dati semi-strutturati dei menu\u003C/h2\u003E\n","\u003Ch3\u003EPanoramica\u003C/h3\u003E\n","\u003Cp\u003ENel ruolo di data engineer di Tasty Bytes, siamo stati incaricati di profilare i dati dei nostri menu, che includono una colonna di dati semi-strutturati. Da questa tabella dei menu dobbiamo produrre una vista del layer analitico che metta a disposizione dei nostri utenti finali i dati relativi alle restrizioni dietetiche e agli ingredienti.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 1 - Impostare il contesto ed eseguire query sulla tabella\u003C/h3\u003E\n","\u003Cp\u003EPer iniziare, eseguiamo insieme le prime tre query per:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EImpostare il contesto del ruolo su \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EImpostare il contesto del warehouse su \u003Ccode\u003Etasty_de_wh\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EProdurre un set di risultati \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/top_n\"\u003ETOP\u003C/a\u003E 10 dalla tabella \u003Ccode\u003Eraw_pos.menu\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer; \nUSE WAREHOUSE tasty_de_wh;\n\nSELECT TOP 10 m.truck_brand_name, m.menu_type, m.menu_item_name, m.menu_item_health_metrics_obj FROM frostbyte_tasty_bytes.raw_pos.menu m; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.1.menu.png\" alt=\"assets/3.1.menu.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENell’output ottenuto, vediamo che la colonna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E riporta i dati semi-strutturati che contengono le metriche che dobbiamo fornire a valle.\u003C/p\u003E\n","\u003Cp\u003EFacendo clic in una delle celle di questa colonna, vedremo che Snowsight espande automaticamente il pannello delle statistiche per fornirci una visuale migliore del contenuto.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.1.2.stats.png\" alt=\"assets/3.1.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 2 - Esplorare la colonna di dati semi-strutturati\u003C/h3\u003E\n","\u003Cp\u003EPer approfondire il modo in cui questa colonna è definita in Snowflake, esegui la prossima query, in cui utilizziamo \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/show-columns\"\u003ESHOW COLUMNS\u003C/a\u003E per esplorare i tipi di dati contenuti nella tabella \u003Ccode\u003Emenu\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.2.show_columns.png\" alt=\"assets/3.2.show_columns.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEsaminando il set di risultati, vediamo che i dati in \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E sono di tipo \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-semistructured\"\u003EVARIANT\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nPer dati generalmente regolari che utilizzano solo tipi di dati nativi del formato semi-strutturato in uso (ad es. string e integer per il formato JSON), i requisiti di storage e le prestazioni delle query per le operazioni sui dati relazionali e sui dati in una colonna VARIANT sono molto simili.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPassaggio 3 - Attraversare i dati semi-strutturati utilizzando la notazione punto\u003C/h3\u003E\n","\u003Cp\u003EAbbiamo visto che la colonna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E includeva \u003Ccode\u003Emenu_item_id\u003C/code\u003E insieme ai dati più nidificati degli ingredienti e delle restrizioni dietetiche che ci servono.\u003C/p\u003E\n","\u003Cp\u003EEsegui la prossima query, in cui iniziamo a utilizzare la \u003Ca href=\"https://docs.snowflake.com/en/user-guide/querying-semistructured#dot-notation\"\u003Enotazione punto\u003C/a\u003E per attraversare i dati semi-strutturati.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.3.dot.png\" alt=\"assets/3.3.dot.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EUtilizzando la notazione punto siamo riusciti a estrarre completamente \u003Ccode\u003Emenu_item_id\u003C/code\u003E, ma a quanto pare rimangono ancora altri oggetti semi-strutturati nell’output della colonna \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EFacciamo nuovamente clic in una delle celle di questa colonna per esaminarla nei dettagli.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.3.2.stats.png\" alt=\"assets/3.3.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStiamo facendo progressi!\u003C/strong\u003E Nella prossima sezione vedremo come possiamo elaborare ulteriormente \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E utilizzando altre funzioni di Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 4 - Fai clic su Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAppiattire i dati semi-strutturati\u003C/h2\u003E\n","\u003Ch3\u003EPanoramica\u003C/h3\u003E\n","\u003Cp\u003EDopo avere visto come interrogare facilmente i dati semi-strutturati contenuti in una colonna Variant utilizzando la notazione punto, il nostro data engineer di Tasty Bytes è sulla buona strada per fornire ai suoi stakeholder interni i dati che hanno richiesto.\u003C/p\u003E\n","\u003Cp\u003EIn questa sezione elaboreremo ulteriormente i dati semi-strutturati per soddisfare i requisiti.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 1 - Introduzione all’appiattimento laterale\u003C/h3\u003E\n","\u003Cp\u003EPer estrarre dalla colonna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E i dati richiesti dagli utenti a valle, esegui la prossima query, che utilizza la funzionalità di notazione punto che abbiamo appena visto insieme alle funzioni \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/flatten\"\u003EFLATTEN\u003C/a\u003E e \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/join-lateral\"\u003ELATERAL JOIN\u003C/a\u003E di Snowflake per fornirci il primo array \u003Ccode\u003Eingredient\u003C/code\u003E che ci è stato richiesto.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EFlatten:\u003C/strong\u003E una funzione per tabelle che produce una vista laterale a partire da una colonna VARIANT, OBJECT o ARRAY. La funzione Flatten può essere utilizzata per convertire dati semi-strutturati in una rappresentazione relazionale.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELateral Join:\u003C/strong\u003E a differenza dell’output di un join non laterale, l’output di un join laterale include solo le righe generate dalla vista in linea. Non è necessario fare il join delle righe sul lato sinistro con quelle sul lato destro, poiché le righe sul lato sinistro sono già state incluse passandole alla vista in linea.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/4.1.lat_flat.png\" alt=\"assets/4.1.lat_flat.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 2 - Esplorare una funzione per array\u003C/h3\u003E\n","\u003Cp\u003EPrima di estrarre i dati sulle restrizioni dietetiche richiesti, esegui la prossima query, in cui viene evidenziata una funzione per array di Snowflake che esplora la colonna \u003Ccode\u003Eingredients\u003C/code\u003E alla ricerca di qualsiasi \u003Ccode\u003Emenu_item_name\u003C/code\u003E che contiene “Lettuce” utilizzando \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/array_contains\"\u003EARRAY_CONTAINS\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:"ingredients"::VARIANT);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/4.2.array_contains.png\" alt=\"assets/4.2.array_contains.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOsservando l’output, vediamo che numerose voci di menu contengono “Lettuce”. Questo tipo di analisi potrebbe essere molto utile ai responsabili degli acquisti in caso di richiami di prodotti alimentari nelle città e nei Paesi che supportiamo.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 3 - Strutturare i dati semi-strutturati su larga scala\u003C/h3\u003E\n","\u003Cp\u003EOra che abbiamo visto che tipo di valore possiamo già offrire alla nostra organizzazione, eseguiamo l’ultima query di questa sezione.\u003C/p\u003E\n","\u003Cp\u003EQuesta query utilizza la notazione punto e la combinazione delle funzioni Lateral Join e Flatten Table per fornire il risultato che ci era stato chiesto di produrre.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/4.3.lat_flat_2.png\" alt=\"assets/4.3.lat_flat_2.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFantastico!\u003C/strong\u003E Questo output soddisfa esattamente i requisiti dei nostri stakeholder. Nella prossima sezione vedremo come promuovere questa query al layer analitico in cui potranno accedervi.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 4 - Fai clic su Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECreare viste strutturate dei dati semi-strutturati\u003C/h2\u003E\n","\u003Ch3\u003EPanoramica\u003C/h3\u003E\n","\u003Cp\u003ENella sezione precedente abbiamo creato una query che fornisce esattamente l’output richiesto dai nostri utenti finali utilizzando una serie di funzionalità di Snowflake per i dati semi-strutturati. Ora vedremo il processo da seguire per promuovere questa query dal layer dei dati grezzi a quello armonizzato e infine a quello analitico, in cui gli utenti finali dispongono dei privilegi necessari per leggere i risultati.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENota:\u003C/strong\u003E per chi ha più familiarità con i modelli Bronze, Silver e Gold, il layer dei dati grezzi corrisponde a Bronze, il layer armonizzato a Silver e il layer analitico a Gold.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 1 - Creare la vista armonizzata utilizzando il codice SQL per l’appiattimento dei dati semi-strutturati\u003C/h3\u003E\n","\u003Cp\u003EPartendo dalla stessa query con cui abbiamo concluso la sezione precedente, esegui la prossima query, che contiene questo codice SQL più tutte le altre colonne della tabella \u003Ccode\u003Emenu\u003C/code\u003E già strutturate.\u003C/p\u003E\n","\u003Cp\u003EIn questa query utilizziamo \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-view\"\u003ECREATE VIEW\u003C/a\u003E nello schema armonizzato per incapsulare la logica di elaborazione dei dati semi-strutturati e le colonne aggiuntive in una tabella.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nUna vista consente di accedere al risultato di una query come se fosse una tabella. Le viste possono essere utilizzate per vari scopi, tra cui combinare, separare e proteggere i dati.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\n    AS\nSELECT \n    m.menu_id,\n    m.menu_type_id,\n    m.menu_type,\n    m.truck_brand_name,\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    m.item_category,\n    m.item_subcategory,\n    m.cost_of_goods_usd,\n    m.sale_price_usd,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/5.1.harmonized_view.png\" alt=\"assets/5.1.harmonized_view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOra che abbiamo creato la vista \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E, possiamo interrogarla direttamente senza ricorrere al codice SQL più complesso che abbiamo utilizzato al suo interno. Tuttavia, poiché i nostri stakeholder accedono ai dati dal layer analitico, eseguiamo il prossimo passaggio per trasferire la vista a tale layer.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 2 - Promuovere facilmente dal layer armonizzato al layer analitico\u003C/h3\u003E\n","\u003Cp\u003EFacendo riferimento a \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E, ora esegui la prossima query per creare la vista \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E, che inizierà immediatamente a fornire valore a valle.\u003C/p\u003E\n","\u003Cp\u003EIn questa query vediamo alcune nuove funzioni che non abbiamo ancora trattato. Per prima cosa aggiungiamo un commento (\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E), che può essere visualizzato nei comandi \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/show\"\u003ESHOW VIEWS\u003C/a\u003E o nell’interfaccia Snowsight per documentare ciò che un utente potrebbe vedere quando interroga questa vista.\u003C/p\u003E\n","\u003Cp\u003EUtilizziamo inoltre i parametri \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/select#parameters\"\u003ESELECT * EXCLUDE e RENAME\u003C/a\u003E, che possono facilitare molto il compito degli sviluppatori SQL riducendo la complessità della definizione di una query o di una vista.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\n\u003Cstrong\u003EExclude:\u003C/strong\u003E quando si selezionano tutte le colonne (SELECT * o SELECT nome_tabella.*), specifica le colonne da escludere dai risultati.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERename:\u003C/strong\u003E quando si selezionano tutte le colonne (SELECT * o SELECT nome_tabella.*), specifica gli alias delle colonne da utilizzare nei risultati.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/5.2.analytics_view.png\" alt=\"assets/5.2.analytics_view.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 3 - Fai clic su Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAnalizzare dati semi-strutturati elaborati in Snowsight\u003C/h2\u003E\n","\u003Ch3\u003EPanoramica\u003C/h3\u003E\n","\u003Cp\u003ECon la vista menu disponibile nel layer analitico, eseguiamo alcune query su questa vista che forniremo ai nostri utenti finali e che mostrano come Snowflake consente di ottenere l’esperienza di una query relazionale su dati semi-strutturati senza dover creare copie aggiuntive o eseguire elaborazioni complesse.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 1 - Analizzare gli array\u003C/h3\u003E\n","\u003Cp\u003ENella sezione precedente abbiamo visto la colonna \u003Ccode\u003Eingredients\u003C/code\u003E come risultato di una query, ma ora è disponibile senza bisogno di utilizzare la notazione punto nella vista \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ECon questa vista, ora esegui la prossima query, che sfrutta altre due funzioni per array di Snowflake, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/array_intersection\"\u003EARRAY_INTERSECTION\u003C/a\u003E e \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/arrays_overlap\"\u003EARRAYS_OVERLAP\u003C/a\u003E, per vedere quali delle voci di menu che non sono bevande si sovrappongono per ciascuno dei menu dei vari marchi dei food truck.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m1.menu_type,\n    m1.menu_item_name,\n    m2.menu_type AS overlap_menu_type,\n    m2.menu_item_name AS overlap_menu_item_name,\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\n    ON m1.menu_item_id <> m2.menu_item_id -- avoid joining the same menu item to itself\n    AND m1.menu_type <> m2.menu_type \nWHERE 1=1\n    AND m1.item_category <> 'Beverage' -- remove beverages\n    AND m2.item_category <> 'Beverage' -- remove beverages\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\nORDER BY m1.menu_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.1.arrays.png\" alt=\"assets/6.1.arrays.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDi nuovo, utilizzando esattamente la vista che abbiamo creato, questo tipo di query potrebbe fornire valore ai nostri operatori di food truck in situazioni reali quando lavorano con il reparto acquisti per prevedere e ordinare gli ingredienti necessari ogni settimana.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 2 - Fornire metriche ai dirigenti\u003C/h3\u003E\n","\u003Cp\u003EOra vediamo come aiutare i dirigenti di Tasty Bytes a prendere decisioni sui menu basate sui dati con metriche di alto livello sulle restrizioni dietetiche attualmente utilizzate per i nostri diversi marchi.\u003C/p\u003E\n","\u003Cp\u003EEsegui la prossima query, che utilizza istruzioni \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/count\"\u003ECOUNT\u003C/a\u003E e \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/sum\"\u003ESUM\u003C/a\u003E e istruzioni \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/case\"\u003ECASE\u003C/a\u003E condizionali per aggregare le metriche richieste provenienti dalla vista \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.2.metrics.png\" alt=\"assets/6.2.metrics.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECon l’output che abbiamo appena ricevuto, siamo passati da una tabella grezza contenente dati semi-strutturati a un’unica riga aggregata a cui chiunque nella nostra organizzazione può accedere facilmente, per potenziare Tasty Bytes e renderla più orientata ai dati.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 3 - Trasformare i risultati in grafici\u003C/h3\u003E\n","\u003Cp\u003EDato che alcuni dei nostri dirigenti di Tasty Bytes preferiscono rappresentazioni visive dei dati, vediamo ora come è facile trasformare i risultati tabellari in grafici visivi facili da comprendere all’interno di Snowsight.\u003C/p\u003E\n","\u003Cp\u003EEsegui la prossima query, che aggiunge filtri per tre dei nostri marchi al codice SQL che abbiamo eseguito in precedenza.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    m.brand_name,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\nGROUP BY m.brand_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.3.results.png\" alt=\"assets/6.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDi default, Snowsight restituisce i risultati delle query sotto forma di tabelle. Tuttavia, una potente funzionalità di Snowsight che non abbiamo ancora trattato è l’\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-visualizations#using-charts\"\u003Eutilizzo dei grafici\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EOra segui le frecce nella schermata qui sotto per creare un grafico a barre che mette a confronto le voci di menu per determinate restrizioni dietetiche dei diversi marchi di food truck.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.3.chart.png\" alt=\"assets/6.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPer completare questo quickstart, dobbiamo evidenziare la facilità con cui un dirigente di Tasty Bytes potrebbe svolgere questo tipo di analisi autonomamente, senza bisogno di sapere nulla dell’elaborazione dei dati semi-strutturati che abbiamo incapsulato nelle nostre viste. In questo modo possiamo essere sicuri di avere contribuito alla democratizzazione dei dati all’interno di Tasty Bytes.\u003C/p\u003E\n","\u003Ch3\u003EPassaggio 4 - Fai clic su Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusione e fasi successive\u003C/h2\u003E\n","\u003Ch3\u003EConclusione\u003C/h3\u003E\n","\u003Cp\u003EOttimo lavoro! Hai completato il quickstart Tasty Bytes - Da zero a Snowflake - Dati semi-strutturati.\u003C/p\u003E\n","\u003Cp\u003EIn questo quickstart hai:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EImparato che cos’è il tipo di dati VARIANT\u003C/li\u003E\u003Cli\u003EUtilizzato la notazione punto per interrogare i dati semi-strutturati\u003C/li\u003E\u003Cli\u003EAppiattito dati semi-strutturati\u003C/li\u003E\u003Cli\u003EAnalizzato array\u003C/li\u003E\u003Cli\u003ECreato due viste\u003C/li\u003E\u003Cli\u003ECreato un grafico a partire da un set di risultati in Snowsight\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESe desideri ripetere questo quickstart, usa gli script di ripristino alla fine del foglio di lavoro associato.\u003C/p\u003E\n","\u003Ch3\u003EFasi successive\u003C/h3\u003E\n","\u003Cp\u003EPer continuare il tuo percorso nel Data Cloud Snowflake, visita il link qui sotto per vedere tutti gli altri quickstart Powered by Tasty Bytes disponibili.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/it/developers/guides/tasty-bytes-introduction-it/\"\u003EQuickstart Powered by Tasty Bytes - Sommario\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Elaborare dati semi-strutturati in Snowflake\r\n\r\n### Panoramica\r\nQuesto quickstart Powered by Tasty Bytes - Da zero a Snowflake è dedicato all’elaborazione dei dati semi-strutturati.\r\n\r\nNel corso del quickstart impareremo come elaborare i dati semi-strutturati in Snowflake esaminando il tipo di dati VARIANT, come elaborare i dati semi-strutturati con una combinazione di notazione punto e appiattimento laterale e come creare viste e grafici in Snowsight.\r\n\r\nPer maggiori dettagli sui dati semi-strutturati in Snowflake, consulta la [documentazione introduttiva sui dati semi-strutturati](https://docs.snowflake.com/en/user-guide/semistructured-concepts)\r\n\r\n### Prerequisiti\r\n- Prima di iniziare, assicurati di avere completato il [**quickstart Introduzione a Tasty Bytes**](/it/developers/guides/tasty-bytes-introduction-it/), che spiega nei dettagli come configurare un account di prova e implementare l’ambiente base di Tasty Bytes necessario per completare questo quickstart.\r\n\r\n### Cosa imparerai\r\n- Che cos’è il tipo di dati VARIANT\r\n- Come utilizzare la notazione punto per interrogare i dati semi-strutturati\r\n- Come appiattire i dati semi-strutturati\r\n- Come analizzare gli array\r\n- Come creare una vista\r\n- Come creare un grafico a partire da un set di risultati in Snowsight\r\n\r\n### Cosa realizzerai\r\n- Un processo di gestione dei dati semi-strutturati end-to-end\r\n- Una vista armonizzata (Silver) e analitica (Gold)\r\n- Un grafico a barre utile per i dirigenti\r\n\r\n## Creare un foglio di lavoro e copiare il codice SQL\r\n\r\n### Panoramica\r\nIn questo quickstart seguiremo la storia del cliente Tasty Bytes attraverso un foglio di lavoro SQL di Snowsight, utilizzando questa pagina come guida completa di commenti aggiuntivi, immagini e link alla documentazione.\r\n\r\nIn questa sezione vedremo nei dettagli come accedere a Snowflake, creare un nuovo foglio di lavoro, rinominarlo, copiare da GitHub il codice SQL che utilizzeremo in questo quickstart e incollarlo nel foglio di lavoro.\r\n\r\n### Passaggio 1 - Accedere a Snowflake tramite URL\r\n- Apri una finestra del browser e inserisci l’URL del tuo account Snowflake \r\n\r\n### Passaggio 2 - Effettuare l’accesso a Snowflake\r\n- Accedi al tuo account Snowflake.\r\n    - ![assets/log_into_snowflake.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/log_into_snowflake.gif)\r\n\r\n### Passaggio 3 - Accedere ai fogli di lavoro\r\n- Fai clic sulla scheda Worksheets nella barra di navigazione sulla sinistra.\r\n    - ![assets/worksheet_tab.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/worksheet_tab.png)\r\n\r\n### Passaggio 4 - Creare un foglio di lavoro\r\n- Nella scheda Worksheets, fai clic sul pulsante “+” nell’angolo superiore destro di Snowsight e scegli “SQL Worksheet”\r\n    - ![assets/+_sqlworksheet.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/+_sqlworksheet.png)\r\n\r\n### Passaggio 5 - Rinominare un foglio di lavoro\r\n- Rinomina il foglio di lavoro facendo clic sul nome generato automaticamente (data e ora) e inserendo “Tasty Bytes - Semi-Structured Data”.\r\n    - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/rename_worksheet_tasty_bytes_setup.gif)\r\n\r\n### Passaggio 6 - Accedere al codice SQL di questo quickstart in GitHub\r\n- Fai clic sul pulsante qui sotto, che ti porterà al file SQL di Tasty Bytes archiviato su GitHub. \u003Cbutton\u003E[tb_zts_semi_structured_data.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql)\u003C/button\u003E\r\n\r\n### Passaggio 7 - Copiare la configurazione SQL da GitHub\r\n- In GitHub, vai sul lato destro e fai clic su “Copy raw contents”. Tutto il codice SQL necessario verrà copiato nei tuoi Appunti.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/github_copy_raw_contents.png)\r\n\r\n### Passaggio 8 - Incollare la configurazione SQL da GitHub nel tuo foglio di lavoro Snowflake\r\n- Torna a Snowsight e al foglio di lavoro che hai appena creato e incolla (*CMD + V per Mac o CTRL + V per Windows*) il codice che abbiamo appena copiato da GitHub.\r\n\r\n### Passaggio 9 - Fai clic su Next --\u003E\r\n\r\n## Profilare i dati semi-strutturati dei menu\r\n\r\n### Panoramica\r\nNel ruolo di data engineer di Tasty Bytes, siamo stati incaricati di profilare i dati dei nostri menu, che includono una colonna di dati semi-strutturati. Da questa tabella dei menu dobbiamo produrre una vista del layer analitico che metta a disposizione dei nostri utenti finali i dati relativi alle restrizioni dietetiche e agli ingredienti.\r\n\r\n### Passaggio 1 - Impostare il contesto ed eseguire query sulla tabella\r\nPer iniziare, eseguiamo insieme le prime tre query per:\r\n- Impostare il contesto del ruolo su `tasty_data_engineer`\r\n- Impostare il contesto del warehouse su `tasty_de_wh`\r\n- Produrre un set di risultati [TOP](https://docs.snowflake.com/en/sql-reference/constructs/top_n) 10 dalla tabella `raw_pos.menu`\r\n\r\n``` \r\nUSE ROLE tasty_data_engineer; \r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT TOP 10 m.truck_brand_name, m.menu_type, m.menu_item_name, m.menu_item_health_metrics_obj FROM frostbyte_tasty_bytes.raw_pos.menu m; \r\n```\r\n\r\n![assets/3.1.menu.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.1.menu.png)\r\n\r\nNell’output ottenuto, vediamo che la colonna `menu_item_health_metrics_obj` riporta i dati semi-strutturati che contengono le metriche che dobbiamo fornire a valle. \r\n\r\nFacendo clic in una delle celle di questa colonna, vedremo che Snowsight espande automaticamente il pannello delle statistiche per fornirci una visuale migliore del contenuto.\r\n\r\n![assets/3.1.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.1.2.stats.png)\r\n\r\n### Passaggio 2 - Esplorare la colonna di dati semi-strutturati\r\nPer approfondire il modo in cui questa colonna è definita in Snowflake, esegui la prossima query, in cui utilizziamo [SHOW COLUMNS](https://docs.snowflake.com/en/sql-reference/sql/show-columns) per esplorare i tipi di dati contenuti nella tabella `menu`.\r\n\r\n```\r\nSHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\r\n```\r\n\r\n![assets/3.2.show_columns.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.2.show_columns.png)\r\n\r\nEsaminando il set di risultati, vediamo che i dati in `menu_item_health_metrics_obj` sono di tipo [VARIANT](https://docs.snowflake.com/en/sql-reference/data-types-semistructured).\r\n\r\n\u003Easide positive\r\nPer dati generalmente regolari che utilizzano solo tipi di dati nativi del formato semi-strutturato in uso (ad es. string e integer per il formato JSON), i requisiti di storage e le prestazioni delle query per le operazioni sui dati relazionali e sui dati in una colonna VARIANT sono molto simili.\r\n\u003E\r\n\r\n### Passaggio 3 - Attraversare i dati semi-strutturati utilizzando la notazione punto\r\nAbbiamo visto che la colonna `menu_item_health_metrics_obj` includeva `menu_item_id` insieme ai dati più nidificati degli ingredienti e delle restrizioni dietetiche che ci servono. \r\n\r\nEsegui la prossima query, in cui iniziamo a utilizzare la [notazione punto](https://docs.snowflake.com/en/user-guide/querying-semistructured#dot-notation) per attraversare i dati semi-strutturati.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\r\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\r\n```\r\n\r\n![assets/3.3.dot.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.3.dot.png)\r\n\r\nUtilizzando la notazione punto siamo riusciti a estrarre completamente `menu_item_id`, ma a quanto pare rimangono ancora altri oggetti semi-strutturati nell’output della colonna `menu_item_health_metrics`. \r\n\r\nFacciamo nuovamente clic in una delle celle di questa colonna per esaminarla nei dettagli.\r\n\r\n![assets/3.3.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/3.3.2.stats.png)\r\n\r\n**Stiamo facendo progressi!** Nella prossima sezione vedremo come possiamo elaborare ulteriormente `menu_item_health_metrics` utilizzando altre funzioni di Snowflake.\r\n\r\n### Passaggio 4 - Fai clic su Next --\u003E\r\n\r\n## Appiattire i dati semi-strutturati\r\n\r\n### Panoramica\r\nDopo avere visto come interrogare facilmente i dati semi-strutturati contenuti in una colonna Variant utilizzando la notazione punto, il nostro data engineer di Tasty Bytes è sulla buona strada per fornire ai suoi stakeholder interni i dati che hanno richiesto.\r\n\r\nIn questa sezione elaboreremo ulteriormente i dati semi-strutturati per soddisfare i requisiti.\r\n\r\n### Passaggio 1 - Introduzione all’appiattimento laterale\r\nPer estrarre dalla colonna `menu_item_health_metrics_obj` i dati richiesti dagli utenti a valle, esegui la prossima query, che utilizza la funzionalità di notazione punto che abbiamo appena visto insieme alle funzioni [FLATTEN](https://docs.snowflake.com/en/sql-reference/functions/flatten) e [LATERAL JOIN](https://docs.snowflake.com/en/sql-reference/constructs/join-lateral) di Snowflake per fornirci il primo array `ingredient` che ci è stato richiesto.\r\n\r\n\u003Easide positive\r\n**Flatten:** una funzione per tabelle che produce una vista laterale a partire da una colonna VARIANT, OBJECT o ARRAY. La funzione Flatten può essere utilizzata per convertire dati semi-strutturati in una rappresentazione relazionale.\r\n\u003E\r\n\u003E**Lateral Join:** a differenza dell’output di un join non laterale, l’output di un join laterale include solo le righe generate dalla vista in linea. Non è necessario fare il join delle righe sul lato sinistro con quelle sul lato destro, poiché le righe sul lato sinistro sono già state incluse passandole alla vista in linea. \r\n\u003E\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.1.lat_flat.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/4.1.lat_flat.png)\r\n\r\n### Passaggio 2 - Esplorare una funzione per array\r\nPrima di estrarre i dati sulle restrizioni dietetiche richiesti, esegui la prossima query, in cui viene evidenziata una funzione per array di Snowflake che esplora la colonna `ingredients` alla ricerca di qualsiasi `menu_item_name` che contiene “Lettuce” utilizzando [ARRAY_CONTAINS](https://docs.snowflake.com/en/sql-reference/functions/array_contains).\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\r\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:\"ingredients\"::VARIANT);\r\n```\r\n\r\n![assets/4.2.array_contains.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/4.2.array_contains.png)\r\n\r\nOsservando l’output, vediamo che numerose voci di menu contengono “Lettuce”. Questo tipo di analisi potrebbe essere molto utile ai responsabili degli acquisti in caso di richiami di prodotti alimentari nelle città e nei Paesi che supportiamo.\r\n\r\n### Passaggio 3 - Strutturare i dati semi-strutturati su larga scala\r\nOra che abbiamo visto che tipo di valore possiamo già offrire alla nostra organizzazione, eseguiamo l’ultima query di questa sezione. \r\n\r\nQuesta query utilizza la notazione punto e la combinazione delle funzioni Lateral Join e Flatten Table per fornire il risultato che ci era stato chiesto di produrre.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.3.lat_flat_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/4.3.lat_flat_2.png)\r\n\r\n**Fantastico!** Questo output soddisfa esattamente i requisiti dei nostri stakeholder. Nella prossima sezione vedremo come promuovere questa query al layer analitico in cui potranno accedervi.\r\n\r\n### Passaggio 4 - Fai clic su Next --\u003E\r\n\r\n## Creare viste strutturate dei dati semi-strutturati\r\n\r\n### Panoramica\r\nNella sezione precedente abbiamo creato una query che fornisce esattamente l’output richiesto dai nostri utenti finali utilizzando una serie di funzionalità di Snowflake per i dati semi-strutturati. Ora vedremo il processo da seguire per promuovere questa query dal layer dei dati grezzi a quello armonizzato e infine a quello analitico, in cui gli utenti finali dispongono dei privilegi necessari per leggere i risultati.\r\n\r\n**Nota:** per chi ha più familiarità con i modelli Bronze, Silver e Gold, il layer dei dati grezzi corrisponde a Bronze, il layer armonizzato a Silver e il layer analitico a Gold.\r\n\r\n### Passaggio 1 - Creare la vista armonizzata utilizzando il codice SQL per l’appiattimento dei dati semi-strutturati\r\nPartendo dalla stessa query con cui abbiamo concluso la sezione precedente, esegui la prossima query, che contiene questo codice SQL più tutte le altre colonne della tabella `menu` già strutturate. \r\n\r\nIn questa query utilizziamo [CREATE VIEW](https://docs.snowflake.com/en/sql-reference/sql/create-view) nello schema armonizzato per incapsulare la logica di elaborazione dei dati semi-strutturati e le colonne aggiuntive in una tabella.\r\n\r\n\u003Easide positive\r\nUna vista consente di accedere al risultato di una query come se fosse una tabella. Le viste possono essere utilizzate per vari scopi, tra cui combinare, separare e proteggere i dati. \r\n\u003E\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\r\n    AS\r\nSELECT \r\n    m.menu_id,\r\n    m.menu_type_id,\r\n    m.menu_type,\r\n    m.truck_brand_name,\r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    m.item_category,\r\n    m.item_subcategory,\r\n    m.cost_of_goods_usd,\r\n    m.sale_price_usd,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/5.1.harmonized_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/5.1.harmonized_view.png)\r\n\r\nOra che abbiamo creato la vista `harmonized.menu_v`, possiamo interrogarla direttamente senza ricorrere al codice SQL più complesso che abbiamo utilizzato al suo interno. Tuttavia, poiché i nostri stakeholder accedono ai dati dal layer analitico, eseguiamo il prossimo passaggio per trasferire la vista a tale layer.\r\n\r\n### Passaggio 2 - Promuovere facilmente dal layer armonizzato al layer analitico\r\nFacendo riferimento a `harmonized.menu_v`, ora esegui la prossima query per creare la vista `analytics.menu_v`, che inizierà immediatamente a fornire valore a valle. \r\n\r\nIn questa query vediamo alcune nuove funzioni che non abbiamo ancora trattato. Per prima cosa aggiungiamo un commento ([COMMENT](https://docs.snowflake.com/en/sql-reference/sql/comment)), che può essere visualizzato nei comandi [SHOW VIEWS](https://docs.snowflake.com/en/sql-reference/sql/show) o nell’interfaccia Snowsight per documentare ciò che un utente potrebbe vedere quando interroga questa vista.\r\n\r\nUtilizziamo inoltre i parametri [SELECT * EXCLUDE e RENAME](https://docs.snowflake.com/en/sql-reference/sql/select#parameters), che possono facilitare molto il compito degli sviluppatori SQL riducendo la complessità della definizione di una query o di una vista.\r\n\r\n\u003Easide positive\r\n**Exclude:** quando si selezionano tutte le colonne (SELECT * o SELECT nome_tabella.*), specifica le colonne da escludere dai risultati.\r\n\u003E\r\n\u003E **Rename:** quando si selezionano tutte le colonne (SELECT * o SELECT nome_tabella.*), specifica gli alias delle colonne da utilizzare nei risultati.\r\n\u003E\r\n\r\n![assets/5.2.analytics_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/5.2.analytics_view.png)\r\n\r\n### Passaggio 3 - Fai clic su Next --\u003E\r\n\r\n## Analizzare dati semi-strutturati elaborati in Snowsight\r\n\r\n### Panoramica\r\nCon la vista menu disponibile nel layer analitico, eseguiamo alcune query su questa vista che forniremo ai nostri utenti finali e che mostrano come Snowflake consente di ottenere l’esperienza di una query relazionale su dati semi-strutturati senza dover creare copie aggiuntive o eseguire elaborazioni complesse.\r\n\r\n### Passaggio 1 - Analizzare gli array\r\nNella sezione precedente abbiamo visto la colonna `ingredients` come risultato di una query, ma ora è disponibile senza bisogno di utilizzare la notazione punto nella vista `analytics.menu_v`. \r\n\r\nCon questa vista, ora esegui la prossima query, che sfrutta altre due funzioni per array di Snowflake, [ARRAY_INTERSECTION](https://docs.snowflake.com/en/sql-reference/functions/array_intersection) e [ARRAYS_OVERLAP](https://docs.snowflake.com/en/sql-reference/functions/arrays_overlap), per vedere quali delle voci di menu che non sono bevande si sovrappongono per ciascuno dei menu dei vari marchi dei food truck.\r\n\r\n```\r\nSELECT \r\n    m1.menu_type,\r\n    m1.menu_item_name,\r\n    m2.menu_type AS overlap_menu_type,\r\n    m2.menu_item_name AS overlap_menu_item_name,\r\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\r\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\r\n    ON m1.menu_item_id \u003C\u003E m2.menu_item_id -- avoid joining the same menu item to itself\r\n    AND m1.menu_type \u003C\u003E m2.menu_type \r\nWHERE 1=1\r\n    AND m1.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND m2.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\r\nORDER BY m1.menu_type;\r\n```\r\n\r\n![assets/6.1.arrays.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.1.arrays.png)\r\n\r\nDi nuovo, utilizzando esattamente la vista che abbiamo creato, questo tipo di query potrebbe fornire valore ai nostri operatori di food truck in situazioni reali quando lavorano con il reparto acquisti per prevedere e ordinare gli ingredienti necessari ogni settimana.\r\n\r\n### Passaggio 2 - Fornire metriche ai dirigenti\r\nOra vediamo come aiutare i dirigenti di Tasty Bytes a prendere decisioni sui menu basate sui dati con metriche di alto livello sulle restrizioni dietetiche attualmente utilizzate per i nostri diversi marchi.\r\n\r\nEsegui la prossima query, che utilizza istruzioni [COUNT](https://docs.snowflake.com/en/sql-reference/functions/count) e [SUM](https://docs.snowflake.com/en/sql-reference/functions/sum) e istruzioni [CASE](https://docs.snowflake.com/en/sql-reference/functions/case) condizionali per aggregare le metriche richieste provenienti dalla vista `analytics.menu_v`.\r\n\r\n```\r\nSELECT\r\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\r\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\r\n```\r\n\r\n![assets/6.2.metrics.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.2.metrics.png)\r\n\r\nCon l’output che abbiamo appena ricevuto, siamo passati da una tabella grezza contenente dati semi-strutturati a un’unica riga aggregata a cui chiunque nella nostra organizzazione può accedere facilmente, per potenziare Tasty Bytes e renderla più orientata ai dati. \r\n\r\n\r\n### Passaggio 3 - Trasformare i risultati in grafici\r\nDato che alcuni dei nostri dirigenti di Tasty Bytes preferiscono rappresentazioni visive dei dati, vediamo ora come è facile trasformare i risultati tabellari in grafici visivi facili da comprendere all’interno di Snowsight. \r\n\r\nEsegui la prossima query, che aggiunge filtri per tre dei nostri marchi al codice SQL che abbiamo eseguito in precedenza.\r\n\r\n```\r\nSELECT\r\n    m.brand_name,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m\r\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\r\nGROUP BY m.brand_name;\r\n```\r\n\r\n![assets/6.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.3.results.png)\r\n\r\nDi default, Snowsight restituisce i risultati delle query sotto forma di tabelle. Tuttavia, una potente funzionalità di Snowsight che non abbiamo ancora trattato è l’[utilizzo dei grafici](https://docs.snowflake.com/en/user-guide/ui-snowsight-visualizations#using-charts).\r\n\r\nOra segui le frecce nella schermata qui sotto per creare un grafico a barre che mette a confronto le voci di menu per determinate restrizioni dietetiche dei diversi marchi di food truck.\r\n\r\n![assets/6.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-it/6.3.chart.png)\r\n\r\nPer completare questo quickstart, dobbiamo evidenziare la facilità con cui un dirigente di Tasty Bytes potrebbe svolgere questo tipo di analisi autonomamente, senza bisogno di sapere nulla dell’elaborazione dei dati semi-strutturati che abbiamo incapsulato nelle nostre viste. In questo modo possiamo essere sicuri di avere contribuito alla democratizzazione dei dati all’interno di Tasty Bytes.\r\n\r\n### Passaggio 4 - Fai clic su Next --\u003E\r\n\r\n## Conclusione e fasi successive\r\n\r\n### Conclusione\r\nOttimo lavoro! 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