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Com latência de pipeline completo em minutos e um mecanismo de processamento incremental eficiente, as Dynamic Tables oferecem uma abordagem moderna e dimensionável para pipelines autônomos. No último ano, a Snowflake lançou uma série de atualizações que tornaram as Dynamic Tables mais rápidas, mais expressivas e mais interoperáveis com as ferramentas que você já usa.\u003C/p\u003E\n\u003Cp\u003ENo Summit, Sergey Labetsik, engenheiro de dados sênior da Wind Creek Hospitality, demonstrou como sua equipe conseguiu entregar vouchers de alimentação aos hóspedes em menos de um minuto após a elegibilidade. Ao migrar um job em lote do dbt para um pipeline de Dynamic Tables, eles reduziram a latência completa para menos de um minuto, uma grande melhoria em relação à programação de 30 minutos em que o job vinha rodando.\u003C/p\u003E\n\u003Cp\u003EVeja a seguir as novidades das Dynamic Tables e por que elas são importantes para os seus pipelines.\u003C/p\u003E\n\u003Ch2\u003EBenchmarks com atualizações até 2,8 vezes mais rápidas\u003C/h2\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy":{"id":"image-448adb0500","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--35dfb348-643e-4b2d-8f34-e85d611bb458/dt-fig1.png?quality=85&preferwebp=true","alt":"Figura 1: Benchmarks mostrando um desempenho de atualização até 2,8x mais rápido nas Dynamic Tables.","lazyEnabled":true,"height":"926","width":"1081","title":"Figura 1: Benchmarks mostrando um desempenho de atualização até 2,8x mais rápido nas Dynamic Tables.",":type":"snowflake-site/components/image"},"blog_text_copy_1178450281":{"id":"blog-text-045cf745bd","additionalClasses":"inline","text":"\u003Cp\u003EA velocidade é a base sobre a qual tudo o mais se constrói. Avaliamos os padrões mais populares de Dynamic Tables de maio de 2025 a maio de 2026 e medimos uma performance de atualização até 2,8 vezes mais rápida. Isso reflete atualizações que fizemos nos bastidores para acelerar a performance das Dynamic Tables, incluindo funções de agregação de nível superior, QUALIFY row/rank = 1 (SCD-1), operações cluster-by e joins, tudo medido em warehouses Gen2.\u003C/p\u003E\n\u003Cp\u003EEsses ganhos vêm de otimizações de performance criadas especificamente para as Dynamic Tables combinadas com warehouses Gen2. As atualizações incrementais processam menos dados, terminam mais cedo e liberam capacidade de processamento para o restante da sua carga de trabalho. Se você ainda não migrou suas cargas de trabalho de Dynamic Tables para warehouses Gen2, agora é a hora.\u003C/p\u003E\n\u003Ch2\u003EPadrões de design: Como construir bem com Dynamic Tables\u003C/h2\u003E\n\u003Cp\u003EVamos voltar ao básico e revisar as \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables/best-practices\"\u003Emelhores práticas para criar pipelines eficientes com Dynamic Tables\u003C/a\u003E:\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EUse várias Dynamic Tables encadeadas:\u003C/b\u003E Divida pipelines complexos em uma cadeia de duas ou mais Dynamic Tables, cada uma cuidando de uma etapa lógica. Muitas equipes usam o vocabulário medallion: bronze (raw landing), prata (\u003Ccode\u003ETARGET_LAG = DOWNSTREAM\u003C/code\u003E) e ouro (agregado com lag baseado em tempo). No mundo das Dynamic Tables, a camada bronze representa a tabela de raw landing, a camada prata é a Dynamic Table onde você limpa os dados e a camada ouro é onde você enriquece os dados, prontos para serem servidos às suas Applications downstream.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EDecomponha joins e agregações em Dynamic Tables separadas:\u003C/b\u003E Coloque os joins primeiro e as agregações em seguida. Cada etapa então atualiza incrementalmente, somando os ganhos de eficiência e melhorando o gerenciamento.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EUse uma estratégia de warehouse duplo:\u003C/b\u003E Defina \u003Ccode\u003EINITIALIZATION_WAREHOUSE\u003C/code\u003E para reinicializações (full scans, que exigem muitos recursos) e um warehouse menor para as atualizações incrementais contínuas.\u003Cbr\u003E\nNunca use \u003Ccode\u003EREFRESH_MODE = AUTO\u003C/code\u003E em produção: use o modo de atualização automática em desenvolvimento para descobrir se o seu pipeline roda incrementalmente ou requer uma atualização completa. Depois, defina o refresh_mode explicitamente em produção.\u003C/p\u003E\n\u003Ch2\u003ENovas atualizações tornam as atualizações ainda mais rápidas\u003C/h2\u003E\n\u003Ch3\u003EDeduplicação SCD-1 com QUALIFY row/rank = 1\u003C/h3\u003E\n\u003Cp\u003EPara pipelines de CDC em que sua tabela base recebe registros append-only, \u003Ccode\u003EQUALIFY ROW_NUMBER() = 1\u003C/code\u003E (GA) é a forma mais limpa de manter incrementalmente apenas a linha mais recente por chave de negócios. A função de janela escolhe a linha correta independentemente da ordem de ingestão, lidando com chegadas fora de ordem sem lógica adicional.\u003C/p\u003E\n\u003Cp\u003EAlém disso, se você usar o padrão \u003Ccode\u003ESELECT * EXCLUDE\u003C/code\u003E, também ganha evolução de schema automática: colunas adicionadas ou removidas da tabela base se propagam automaticamente para o downstream sem alterar a definição da Dynamic Table.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-765c657a7f","language":"sql","codeSnippet":"SELECT * EXCLUDE _metadata_cols\r\nFROM raw_events\r\nQUALIFY ROW_NUMBER() OVER (\r\n  PARTITION BY id\r\n  ORDER BY updated_at DESC\r\n) = 1","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_copy_copy":{"id":"blog-text-8a463134a6","additionalClasses":"inline","text":"\u003Ch3\u003EChaves primárias: resolva insert-overwrites com elegância\u003C/h3\u003E\n\u003Cp\u003EVeja uma história comum: você tem um pipeline incremental rodando com elegância. Então um \u003Ccode\u003EINSERT OVERWRITE\u003C/code\u003E atinge uma tabela base e redefine os metadados de change-tracking. Ou há uma agregação complexa que exige uma atualização completa. Agora, toda tabela downstream está reprocessando tudo do zero. Você pode resolver isso com uma simples restrição \u003Ccode\u003EPRIMARY KEY RELY\u003C/code\u003E (GA) na tabela base.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_678731718":{"id":"code-snippet-8af747fba5","language":"sql","codeSnippet":"ALTER TABLE dim_customers\r\n  ADD PRIMARY KEY (customer_id) RELY;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_copy_copy_767298575":{"id":"blog-text-b83c1f9cb3","text":"\u003Cp\u003EIsso diz à Snowflake: \"Confie nesta chave para detecção de mudanças. Não dependa das colunas de change-tracking.\"\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_copy_64665433":{"id":"blog-text-9eaeacf755","additionalClasses":"inline","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EBenefício\u003C/th\u003E\n\u003Cth\u003EDetalhes\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EPropagação por todo o pipeline\u003C/td\u003E\n\u003Ctd\u003EDeclare uma vez na tabela base; o benefício flui para todas as Dynamic Tables downstream. \u003Ci\u003EObservação: a Primary Key RELY não se aplica retroativamente. Depois de adicioná-la, execute \u003Ccode\u003ECREATE OR REPLACE\u003C/code\u003E nas Dynamic Tables downstream para ativar o benefício.\u003C/i\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EChaves derivadas\u003C/td\u003E\n\u003Ctd\u003EA Snowflake lê automaticamente suas colunas \u003Ccode\u003ESELECT, GROUP BY\u003C/code\u003E e partições \u003Ccode\u003EQUALIFY ROW_NUMBER()\u003C/code\u003E que se tornam a chave única.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EFontes Apache Iceberg™ v2\u003C/td\u003E\n\u003Ctd\u003EMelhora significativamente a performance de update/delete para tabelas no armazenamento na nuvem.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EQuebra a dependência em cascata\u003C/td\u003E\n\u003Ctd\u003EAs Dynamic Tables downstream permanecem \u003Ccode\u003EINCREMENTAL\u003C/code\u003E mesmo ao ler de um pai com atualização \u003Ccode\u003EFULL\u003C/code\u003E.\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_copy_1622672220":{"id":"blog-text-a18aa258a6","text":"\u003Ch2\u003EModo de atualização adaptativo\u003C/h2\u003E\n\u003Cp\u003EE se você não puder usar chaves primárias, mas seu pipeline incremental ocasionalmente atinge condições em que uma recomputação completa seria, na verdade, mais barata? Esse é o problema que o modo de atualização adaptativo (versão preliminar pública) resolve.\u003C/p\u003E\n\u003Cp\u003EPense nele como incremental, mas mais inteligente. A Snowflake tem heurísticas integradas que avaliam, a cada atualização, se devem processar incrementalmente ou reinicializar, com base no que oferecerá a melhor relação custo-performance naquele momento.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_903728357":{"id":"code-snippet-3a7f43d294","language":"sql","codeSnippet":"CREATE DYNAMIC TABLE my_table\r\n  REFRESH_MODE = ADAPTIVE\r\n  TARGET_LAG = '10 minutes'\r\n  WAREHOUSE = my_warehouse\r\nAS\r\n  SELECT ... FROM source_table;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_spacer":{"id":"blog-spacer-b3d2471a8b",":type":"snowflake-site/components/blog/blog-spacer"},"quote_item":{"id":"quote-item-6c021ee294","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"Guardrails built in for adaptive refresh mode: Expensive functions (Cortex AI Functions, user-defined functions) are never reinitialized unexpectedly. If a Dynamic Table definition can't support incremental refresh, creation fails proactively. This helps reduce unexpected runtime behavior.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_copy_copy_328125800":{"id":"blog-text-a29af1b571","text":"\u003Ch3\u003EMascaramento e Row Access Policies em Dynamic Tables incrementais\u003C/h3\u003E\n\u003Cp\u003ESe você atua em serviços financeiros, saúde ou qualquer setor regulado, provavelmente usa políticas de mascaramento ou de acesso a linhas. Anteriormente, certas funções de política exigiam uma atualização completa mesmo quando a própria consulta era elegível para processamento incremental.\u003C/p\u003E\n\u003Cp\u003EQuando ficar disponível ao público (em breve), espera-se que quaisquer Row Access Policies ou políticas de mascaramento que concedam acesso total aos dados à função de proprietário da Dynamic Table passem a oferecer suporte a atualizações incrementais, eliminando full scans desnecessários.\u003C/p\u003E\n\u003Ch2\u003EExpressividade aprimorada: crie modelos mais sofisticados com Dynamic Tables\u003C/h2\u003E\n\u003Ch4\u003EFrozen regions: pare de pagar por dados que não mudam\u003C/h4\u003E\n\u003Cp\u003EImagine cinco anos de dados históricos de pedidos no seu pipeline. A cada atualização, a Snowflake reprocessa tudo, incluindo as linhas de 2020 que nunca vão mudar. As frozen regions (GA) permitem declarar quais linhas podem ser congeladas com um predicado simples. A Snowflake ignora as linhas congeladas em cada atualização. Apenas a janela mutável com dados novos é recomputada.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_833865878":{"id":"code-snippet-1cf5beb6a1","language":"sql","codeSnippet":"CREATE DYNAMIC TABLE orders_enriched\r\n  FROZEN WHERE order_date \u003C CURRENT_DATE() - 1\r\n  TARGET_LAG = '1 hour'\r\n  WAREHOUSE = my_warehouse\r\nAS\r\n  SELECT o.*, c.region\r\n  FROM orders o\r\n  JOIN customers c ON o.customer_id = c.id;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1065001317":{"id":"blog-text-e75398d06f","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EBenefício\u003C/th\u003E\n\u003Cth\u003EO que significa\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EPague apenas pela mudança\u003C/td\u003E\n\u003Ctd\u003EAs linhas congeladas são ignoradas em cada atualização.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EOs deletes são ignorados\u003C/td\u003E\n\u003Ctd\u003EA exclusão de linhas de origem não se propaga para a saída congelada.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EEstabilidade de dimensão\u003C/td\u003E\n\u003Ctd\u003EOs resultados de join congelados não são recomputados, mesmo que as dimensões mudem.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EEconomia em atualizações completas\u003C/td\u003E\n\u003Ctd\u003EAs Dynamic Tables com atualização completa e frozen regions se comportam como atualizações incrementais para dados históricos.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EEvolução de consultas\u003C/td\u003E\n\u003Ctd\u003EApenas as linhas novas são recomputadas quando você evolui a consulta.\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_copy_590942093":{"id":"blog-text-3f2c508839","additionalClasses":"inline","text":"\u003Ch2\u003EBackfill: migre sem reprocessamento\u003C/h2\u003E\n\u003Cp\u003EEstá criando um novo pipeline, mas já tem anos de dados históricos limpos? O \u003Ccode\u003EBACKFILL FROM\u003C/code\u003E faz a clonagem com cópia zero dos dados existentes diretamente na frozen region, sem recomputação (GA).\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_154635758":{"id":"code-snippet-26b28cf58e","language":"sql","codeSnippet":"CREATE DYNAMIC TABLE new_pipeline_table\r\n  FROZEN WHERE event_date \u003C CURRENT_DATE()\r\n  BACKFILL FROM existing_historical_table\r\n  TARGET_LAG = '1 hour'\r\n  WAREHOUSE = my_warehouse\r\nAS\r\n  SELECT * FROM raw_events;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_copy_copy_1030303712":{"id":"blog-text-c2ce02f3ca","additionalClasses":"inline","text":"\u003Cp\u003EMigre pipelines existentes para Dynamic Tables em minutos, não em horas, sem reprocessar anos de dados. Por isso, se você tem dados históricos, recomendamos fortemente que use frozen regions desde o início. Também ouvimos de clientes que ocasionalmente querem atualizar alguma mudança nos dados históricos ou têm um caso de uso de RGPD de excluir linhas específicas. Agora, você pode usar comandos DML nessas regiões congeladas para manter sua Dynamic Table atualizada sem pagar o custo da recomputação.\u003C/p\u003E\n\u003Ch2\u003EPolíticas de ciclo de vida do armazenamento: expire dados brutos e mantenha agregações\u003C/h2\u003E\n\u003Cp\u003EOs dados de eventos brutos têm prazo de validade. Você não precisa de três anos de logs brutos ocupando espaço em uma tabela de pipeline em uso ativo. Mas a limpeza desses dados sem comprometer as agregações downstream pode se transformar em um projeto de engenharia à parte.\u003C/p\u003E\n\u003Cp\u003EAs Storage Lifecycle Policies (GA) permitem que você anexe uma regra de retenção com uma única cláusula. As linhas expiram automaticamente em seu próprio cronograma, de forma independente do refresh. Sem jobs personalizados. Sem instruções \u003Ccode\u003EDELETE\u003C/code\u003E. Sem risco de reprocessar acidentalmente dados já removidos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy_269755218":{"id":"image-e3457b2c9a","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--550a6e68-9618-464b-8df8-a66847d7a141/dt-fig2.png?quality=85&preferwebp=true","alt":"Figure 2: A power architectural pattern for Dynamic Tables pairs storage lifecycle policies with FROZEN REGIONS to delete raw data on a set schedule while protecting aggregates. (Image generated with ChatGPT on 6/11/2026)","lazyEnabled":true,"height":"1111","width":"2048","title":"Figure 2: A power architectural pattern for Dynamic Tables pairs storage lifecycle policies with FROZEN REGIONS to delete raw data on a set schedule while protecting aggregates. (Image generated with ChatGPT on 6/11/2026)",":type":"snowflake-site/components/image"},"blog_text_copy":{"id":"blog-text-a74c8acb9f","text":"\u003Ch2\u003ELimites de refresh: execute pipelines independentes\u003C/h2\u003E\n\u003Cp\u003EPor default, as Dynamic Tables em um pipeline compartilham o isolamento de snapshot, no qual todos os refreshes são sincronizados e leem a partir do mesmo ponto no tempo. Isso é ótimo para a consistência. Mas, às vezes, você precisa de independência.\u003C/p\u003E\n\u003Cp\u003EOs limites de refresh (GA) permitem que você insira uma quebra suave no seu grafo acíclico dirigido (DAG), incluindo o padrão Dynamic Table → View → Dynamic Table, o que permite que subpipelines operem em seus próprios cronogramas de atualização.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_747468253":{"id":"blog-text-3324c54f07","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ECaso de uso\u003C/th\u003E\n\u003Cth\u003EPor que ajuda\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EDimensões de lenta variação\u003C/td\u003E\n\u003Ctd\u003ECatálogos de produtos ou consultas geográficas não bloqueiam pipelines de pedidos de rápida variação.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPipelines entre equipes\u003C/td\u003E\n\u003Ctd\u003EAs falhas da equipe A não se propagam para a equipe B.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EData sharing\u003C/td\u003E\n\u003Ctd\u003EVocê controla seu próprio cronograma de refresh, de forma independente do provedor de dados sobre o qual talvez não tenha controle.\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_2089137133":{"id":"blog-text-98c5e114ea","additionalClasses":"inline","text":"\u003Ch2\u003EDynamic Tables incrementais personalizadas\u003C/h2\u003E\n\u003Cp\u003EE se o seu pipeline precisar de uma lógica \u003Ccode\u003EMERGE\u003C/code\u003E complexa, um join entre transmissão e dados estáticos, soft deletes ou acumuladores em execução? As Dynamic Tables padrão baseadas em \u003Ccode\u003ESELECT\u003C/code\u003E nem sempre conseguem expressar isso de forma elegante, mas você ainda quer agendamento gerenciado, novas tentativas e monitoramento.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-c3abd38ba0","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b74131a7-fe7d-4b4e-a872-924195ee85e4/dt-fig3.png?quality=85&preferwebp=true","alt":"Figure 3: Dynamic Tables now support custom logic (SELECT / MERGE / INSERT) enabling more expressive power alongside the power of the declarative approach Dynamic Tables take. ","lazyEnabled":true,"height":"729","width":"2048","title":"Figure 3: Dynamic Tables now support custom logic (SELECT / MERGE / INSERT) enabling more expressive power alongside the power of the declarative approach Dynamic Tables take.",":type":"snowflake-site/components/image"},"blog_text_copy_1338074972":{"id":"blog-text-c5e9ac9704","additionalClasses":"inline","text":"\u003Cp\u003EAs Dynamic Tables incrementais personalizadas (em breve disponíveis ao público) levam você de uma estratégia exclusivamente \u003Ccode\u003ESELECT\u003C/code\u003E para uma estratégia \u003Ccode\u003ESELECT / MERGE / INSERT\u003C/code\u003E. Você obtém todo o poder expressivo enquanto o Snowflake continua controlando a execução (agendamento gerenciado, rastreamento de dependências, observabilidade e replicação), sem sacrificar a performance e o controle do processamento em lote imperativo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1203981182":{"id":"code-snippet-c6944675b4","language":"sql","codeSnippet":"CREATE DYNAMIC TABLE order_enriched\r\n  REFRESH_MODE = INCREMENTAL\r\n  TARGET_LAG = '5 minutes'\r\n  WAREHOUSE = my_warehouse\r\nAS\r\n  MERGE INTO order_enriched t\r\n  USING (\r\n    SELECT s.order_id, s.amount, d.region\r\n    FROM orders_stream s\r\n    JOIN dim_customers d ON s.customer_id = d.id\r\n  ) src ON t.order_id = src.order_id\r\n  WHEN MATCHED THEN UPDATE SET\r\n    t.amount = src.amount, t.region = src.region\r\n  WHEN NOT MATCHED THEN INSERT\r\n    (order_id, amount, region) VALUES (src.order_id, src.amount, src.region);","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_copy_933480363":{"id":"blog-text-ff0f15cf67","text":"\u003Cp\u003EAs Dynamic Tables com incrementalização personalizada podem ser combinadas com Dynamic Tables padrão baseadas em SELECT no mesmo pipeline. Os casos de uso comuns envolvem:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_342256718":{"id":"blog-text-c381fd0f71","additionalClasses":"inline","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EPadrão\u003C/th\u003E\n\u003Cth\u003ETipo de CI-DT\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EEnriquecimento de CDC com consulta de dimensão e propagação de delete\u003C/td\u003E\n\u003Ctd\u003E\u003Ccode\u003EMERGE\u003C/code\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EManter um leaderboard Top-K\u003C/td\u003E\n\u003Ctd\u003E\u003Ccode\u003EMERGE\u003C/code\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EEnriquecimento de join entre transmissão e dados estáticos apenas com anexação\u003C/td\u003E\n\u003Ctd\u003E\u003Ccode\u003EINSERT\u003C/code\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMigrar de Tasks e Streams\u003C/td\u003E\n\u003Ctd\u003E\u003Ccode\u003EMERGE\u003C/code\u003E ou \u003Ccode\u003EINSERT\u003C/code\u003E\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_1356026585":{"id":"blog-text-de0352eeb9","text":"\u003Ch2\u003EInteroperabilidade\u003C/h2\u003E\n\u003Ch3\u003ESkill CoCo para Dynamic Tables\u003C/h3\u003E\n\u003Cp\u003EDepurar pipelines não deveria exigir a leitura linha por linha dos logs de histórico de refresh. A nova skill de Dynamic Tables no Snowflake CoCo oferece orientação especializada para criar, otimizar, monitorar e solucionar problemas de Dynamic Tables diretamente no seu IDE.\u003C/p\u003E\n\u003Cp\u003EDescreva seu objetivo, mencione as Dynamic Tables e a skill é acionada. Obtenha ajuda rápida com falhas de refresh, ajuste de lag, design de pipeline e otimização de performance.\u003C/p\u003E\n\u003Ch3\u003ECompatibilidade com Apache Iceberg\u003C/h3\u003E\n\u003Cp\u003EAs Dynamic Tables são totalmente compatíveis com Apache Iceberg em ambas as extremidades do seu pipeline.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_1262586085":{"id":"blog-text-426952e485","additionalClasses":"inline","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ECaso de uso\u003C/th\u003E\n\u003Cth\u003EO que você pode fazer com as Dynamic Tables\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003ELer do Apache Iceberg\u003C/td\u003E\n\u003Ctd\u003EUse tabelas Iceberg v2 gerenciadas externamente como fontes. As restrições \u003Ccode\u003EPRIMARY KEY RELY\u003C/code\u003E melhoram significativamente a performance de update/delete aqui.\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EGravar no Apache Iceberg\u003C/td\u003E\n\u003Ctd\u003ECrie Dynamic Iceberg Tables com saída em formato Iceberg em armazenamento externo gerenciado pelo Snowflake, legível diretamente pelo Apache Spark™, Trino e outros mecanismos.\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_copy_257331917":{"id":"blog-text-42813b6ee4","text":"\u003Cp\u003EOs mesmos modos de refresh, o mesmo agendamento e a mesma semântica de pipeline, com uma saída em formato aberto.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_620276962":{"id":"quote-item-97745644c6","alignment":"left","layout":"card","quoteSourceName":"Raimund Kämmerer","quoteSourceTitle":"Staff Data Engineer, Wolt","showQuoteIcon":true,"quote":"A Wolt (parte da DoorDash) adotou o Apache Iceberg como padrão para nos dar a flexibilidade de rodar cada carga de trabalho na engine certa. Usamos as Snowflake Dynamic Iceberg Tables para enriquecer, preparar e atualizar automaticamente os dados em nosso data lake — definimos uma única query com uma recência alvo (target freshness) e o Snowflake gerencia as atualizações incrementais e a orquestração. Com as Dynamic Tables no Apache Iceberg, lançamos pipelines mais rápido, reduzimos o tempo de manutenção e diminuímos o overhead dos nossos pipelines incrementais.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_copy_1130554411":{"id":"blog-text-dbddc87125","text":"\u003Ch3\u003EDynamic Tables como uma materialização do dbt\u003C/h3\u003E\r\n\u003Cp\u003EAlgumas equipes não precisam de refresh contínuo. Elas precisam de dados prontos às 5h, ou estão executando pipelines a partir do dbt ou do Airflow. O \u003Ccode\u003ETARGET_LAG\u003C/code\u003E não funciona bem nesses casos.\u003Cbr\u003E\r\nNovos recursos do scheduler:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Edesative o scheduler para pipelines orquestrados externamente\u003C/li\u003E\r\n\u003Cli\u003ESuporte a refresh síncrono com projetos do Snowflake Database Change Management (DCM)\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003Edbt + Dynamic Tables: melhores juntos\u003C/h3\u003E\r\n\u003Cp\u003EO dbt cuida do seu fluxo de trabalho de engenharia. As Dynamic Tables cuidam da atualização dos seus dados. Juntos, eles eliminam o trade-off entre disciplina de pipeline e performance de pipeline.\u003C/p\u003E\r\n\u003Cp\u003EA integração é uma substituição direta: A maioria dos modelos dbt já são instruções CTAS. Converta-os em Dynamic Tables sem nenhuma reescrita de lógica. Você mantém todo o seu grafo de linhagem, conjuntos de testes e documentação do dbt, enquanto ganha o comportamento automático de pular fontes inalteradas:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Esem reescritas de lógica para converter modelos existentes diretamente\u003C/li\u003E\r\n\u003Cli\u003Eo Snowflake identifica fontes inalteradas e pula o refresh automaticamente\u003C/li\u003E\r\n\u003Cli\u003Elinhagem, testes e documentação completos do dbt preservados\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EVocê pode ler mais sobre essa combinação poderosa neste \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://medium.com/snowflake/dynamic-tables-dbt-a-powerful-combination-f550ebc23d60\"\u003Eblog\u003C/a\u003E.\u003C/p\u003E\r\n\u003Ch2\u003EO que isso significa para os seus pipelines\u003C/h2\u003E\r\n\u003Cp\u003EEssas atualizações somam uma mensagem clara: as Dynamic Tables dão a você mais controle sem mais complexidade. Agora você pode:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eusar \u003Ccode\u003EMERGE\u003C/code\u003E e \u003Ccode\u003EINSERT\u003C/code\u003E para transformações que exigem lógica complexa\u003C/li\u003E\r\n\u003Cli\u003Egerenciar Dynamic Tables no dbt e no Git\u003C/li\u003E\r\n\u003Cli\u003Earmazenar resultados em formato Iceberg para interoperabilidade com Data Lake\u003C/li\u003E\r\n\u003Cli\u003Eenriquecer dados com Cortex AI Functions de forma inline\u003C/li\u003E\r\n\u003Cli\u003Efiltrar por horário atual para janelas móveis\u003C/li\u003E\r\n\u003Cli\u003Edeixar os esquemas evoluírem automaticamente\u003C/li\u003E\r\n\u003Cli\u003Eobter ajuda assistida por IA para criar e depurar pipelines\u003C/li\u003E\r\n\u003Cli\u003Eexecutar refreshes mais rápido em warehouses Gen2\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch1\u003EComece agora\u003C/h1\u003E\r\n\u003Cp\u003EExperimente os novos recursos hoje mesmo:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-about\"\u003EDocumentação das Dynamic Tables\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables/custom-incrementalization\"\u003EGuia de incrementalização personalizada\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables/refresh-modes#adaptive-refresh\"\u003EAdaptive Refresh Mode\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-create-iceberg\"\u003EDynamic Iceberg Tables\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables/dbt\"\u003EAdaptador dbt-snowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESe você já está executando Dynamic Tables, faça a atualização do seu warehouse para a Gen2 e meça a diferença. Se você estava adiando por causa da complexidade de orquestração ou de lacunas de expressividade, esses obstáculos não existem mais.\u003C/p\u003E\r\n\u003Chr\u003E\r\n\r\n\u003Cp\u003EDeclarações prospectivas\u003Cbr\u003E\r\nEste conteúdo contém declarações prospectivas, inclusive sobre nossas futuras ofertas de produtos, e não representa um compromisso de entrega de quaisquer ofertas de produtos. Os resultados e as ofertas reais podem diferir e estão sujeitos a riscos e incertezas conhecidos e desconhecidos. 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