{"id":16931,"date":"2025-02-28T12:42:35","date_gmt":"2025-02-28T20:42:35","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=16931"},"modified":"2025-02-28T12:42:35","modified_gmt":"2025-02-28T20:42:35","slug":"normalization-vs-denormalization","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/normalization-vs-denormalization\/","title":{"rendered":"Compara\u00e7\u00e3o entre normaliza\u00e7\u00e3o de dados e desnormaliza\u00e7\u00e3o"},"content":{"rendered":"<h2>O que \u00e9 normaliza\u00e7\u00e3o de dados?<\/h2>\n<p>A normaliza\u00e7\u00e3o de dados \u00e9 o processo de estrutura\u00e7\u00e3o de um banco de dados para aumentar a efici\u00eancia, manter a consist\u00eancia e eliminar dados redundantes. A ideia \u00e9 simples: dividir os dados em tabelas menores e relacionadas para minimizar a repeti\u00e7\u00e3o e simplificar as atualiza\u00e7\u00f5es. Ao seguir um conjunto de regras chamadas \"formas normais\", a normaliza\u00e7\u00e3o ajuda a evitar a redund\u00e2ncia e a manter a consist\u00eancia em um banco de dados.<\/p>\n<h3>Exemplo de dados normalizados<\/h3>\n<p>Imagine que voc\u00ea esteja gerenciando um banco de dados de uma universidade. Voc\u00ea precisa rastrear os alunos, os cursos que eles fazem e suas matr\u00edculas. Veja como voc\u00ea organizaria esses dados em uma tabela normalizada:<\/p>\n<p><b>Estudantes <\/b>mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td>ID do aluno<\/td>\n<td>Nome<\/td>\n<td>E-mail<\/td>\n<td>Maior<\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>Alice Johnson<\/td>\n<td>alice@univ.edu<\/td>\n<td>Ci\u00eancia da Computa\u00e7\u00e3o<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>John Smith<\/td>\n<td>john@univ.edu<\/td>\n<td>Matem\u00e1tica<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Cursos <\/b>mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td>CourseID<\/td>\n<td>Nome do curso<\/td>\n<td>Cr\u00e9ditos<\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>CS101<\/td>\n<td>Introdu\u00e7\u00e3o ao CS<\/td>\n<td>3<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>MATH101<\/td>\n<td>C\u00e1lculo I<\/td>\n<td>4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Registros <\/b>tabela (vincula alunos e cursos):\u00a0<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td>ID de registro<\/td>\n<td>ID do aluno<\/td>\n<td>CourseID<\/td>\n<td>Semestre<\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>CS101<\/td>\n<td>Outono de 2025<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>2<\/td>\n<td>MATH101<\/td>\n<td>Outono de 2025<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Agora, imagine atualizar o endere\u00e7o de e-mail de Alice em um banco de dados. Sem a normaliza\u00e7\u00e3o, voc\u00ea precisaria alterar o endere\u00e7o de e-mail dela em todos os lugares em que ele aparece, um processo potencialmente propenso a erros. Com a normaliza\u00e7\u00e3o, as altera\u00e7\u00f5es aparecem em todos os lugares quando ela atualiza seu endere\u00e7o de e-mail no banco de dados <b>Estudantes <\/b>tabela. Isso mant\u00e9m os dados consistentes em todo o banco de dados.<\/p>\n<h2>O que \u00e9 desnormaliza\u00e7\u00e3o de dados?<\/h2>\n<p>A desnormaliza\u00e7\u00e3o de dados introduz intencionalmente a redund\u00e2ncia em um banco de dados para melhorar o desempenho da leitura, acelerar as consultas e reduzir a sobrecarga computacional. Diferentemente da normaliza\u00e7\u00e3o, que estrutura os dados para eliminar a duplica\u00e7\u00e3o e manter a consist\u00eancia, a desnormaliza\u00e7\u00e3o <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/sql-server\/\">reduz a necessidade de uni\u00f5es complexas<\/a> armazenando dados relacionados em uma \u00fanica tabela ou documento.<\/p>\n<h3>Exemplo de dados desnormalizados<\/h3>\n<p>Lembra-se do banco de dados da universidade que criamos anteriormente? Esta \u00e9 a apar\u00eancia de uma vers\u00e3o desnormalizada dos dados:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>ID de registro<\/b><\/td>\n<td><b>Estudante<\/b><\/p>\n<p><b>ID<\/b><\/td>\n<td><b>Nome<\/b><\/td>\n<td><b>E-mail<\/b><\/td>\n<td><b>Maior<\/b><\/td>\n<td><b>CourseID<\/b><\/td>\n<td><b>Nome do curso<\/b><\/td>\n<td><b>Cr\u00e9ditos<\/b><\/td>\n<td><b>Semestre<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>Alice Johnson<\/td>\n<td>alice@univ.edu<\/td>\n<td>Ci\u00eancia da Computa\u00e7\u00e3o<\/td>\n<td>CS101<\/td>\n<td>Introdu\u00e7\u00e3o ao CS<\/td>\n<td>3<\/td>\n<td>Outono de 2025<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>2<\/td>\n<td>John Smith<\/td>\n<td>john@univ.edu<\/td>\n<td>Matem\u00e1tica<\/td>\n<td>MATH101<\/td>\n<td>C\u00e1lculo I<\/td>\n<td>4<\/td>\n<td>Outono de 2025<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>As informa\u00e7\u00f5es de Alice aparecem ao lado de todos os cursos em que ela est\u00e1 matriculada. Da mesma forma, os detalhes do curso, como CourseName e Credits, s\u00e3o repetidos para cada aluno, o que torna esse formul\u00e1rio adequado para opera\u00e7\u00f5es de leitura intensa, como a exibi\u00e7\u00e3o de matr\u00edculas em cursos de um aluno em um painel.<\/p>\n<h2>Dados normalizados vs. desnormalizados<\/h2>\n<p>Aqui est\u00e1 um detalhamento conciso das diferen\u00e7as entre dados normalizados e desnormalizados:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Aspecto<\/b><\/td>\n<td><b>Dados normalizados<\/b><\/td>\n<td><b>Dados desnormalizados<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Estrutura<\/b><\/td>\n<td>Organizado em v\u00e1rias tabelas relacionadas.<\/td>\n<td>Combinados em menos tabelas com redund\u00e2ncia.<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Redund\u00e2ncia<\/b><\/td>\n<td>Redund\u00e2ncia m\u00ednima.<\/td>\n<td>Alta redund\u00e2ncia.<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Complexidade da consulta<\/b><\/td>\n<td>Requer JOINs para consultas.<\/td>\n<td>Consultas simplificadas com menos uni\u00f5es.<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Armazenamento<\/b><\/td>\n<td>Economiza espa\u00e7o ao evitar a duplica\u00e7\u00e3o.<\/td>\n<td>Requer mais armazenamento devido \u00e0 redund\u00e2ncia.<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Desempenho<\/b><\/td>\n<td>Otimizado para opera\u00e7\u00f5es de grava\u00e7\u00e3o.<\/td>\n<td>Otimizado para opera\u00e7\u00f5es de leitura.<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td><b>Consist\u00eancia<\/b><\/td>\n<td>Mais f\u00e1cil de manter a integridade dos dados.<\/td>\n<td>Maior risco de inconsist\u00eancias de dados.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Como normalizar dados<\/h2>\n<p>Digamos que voc\u00ea seja respons\u00e1vel por projetar um esquema de banco de dados para uma empresa de entrega de pizza. Seu projeto inicial pode ser parecido com este:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderID<\/b><\/td>\n<td><b>Nome do cliente<\/b><\/td>\n<td><b>Endere\u00e7o<\/b><\/td>\n<td><b>Telefone<\/b><\/td>\n<td><b>PizzaDetails<\/b><\/td>\n<td><b>TotalPrice<\/b><\/td>\n<td><b>Data de entrega<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123 Elm St.<\/td>\n<td>123-456-7892<\/td>\n<td>Pepperoni, grande; Margherita, m\u00e9dio<\/td>\n<td>$25.00<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>Alice Johnson<\/td>\n<td>456 Oak St.<\/td>\n<td>987-654-3211<\/td>\n<td>Veggie, Pequeno<\/td>\n<td>$12.00<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Vamos analisar como normalizar esse esquema passo a passo, passando-o por v\u00e1rias formas de normaliza\u00e7\u00e3o.<\/p>\n<h3>Formul\u00e1rios de normaliza\u00e7\u00e3o de banco de dados<\/h3>\n<h4>Primeira forma normal (1NF)<\/h4>\n<p>Para que um banco de dados esteja na primeira forma normal, cada valor em cada coluna de cada tabela deve ser at\u00f4mico, ou seja, cada valor deve representar apenas uma coisa. Se alterarmos nosso esquema de servi\u00e7o de entrega de pizza para ficar em conformidade com a 1NF, ele ter\u00e1 a seguinte apar\u00eancia:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderID<\/b><\/td>\n<td><b>Nome do cliente<\/b><\/td>\n<td><b>Endere\u00e7o<\/b><\/td>\n<td><b>Telefone<\/b><\/td>\n<td><b>PizzaType<\/b><\/td>\n<td><b>Tamanho<\/b><\/td>\n<td><b>Pre\u00e7o<\/b><\/td>\n<td><b>Data de entrega<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123 Elm St.<\/td>\n<td>123-456-7892<\/td>\n<td>Pepperoni<\/td>\n<td>Grande<\/td>\n<td>15.00<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123 Elm St.<\/td>\n<td>123-456-7892<\/td>\n<td>Margherita<\/td>\n<td>M\u00e9dio<\/td>\n<td>10.00<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>Alice Johnson<\/td>\n<td>456 Oak St.<\/td>\n<td>987-654-3211<\/td>\n<td>Vegetariano<\/td>\n<td>Pequeno<\/td>\n<td>12.00<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Observe que dividimos a coluna PizzaDetails em duas colunas separadas: PizzaType e Size. Isso garante que todas as colunas armazenem valores at\u00f4micos.<\/p>\n<h4>Segunda forma normal (2NF)<\/h4>\n<p>Uma tabela est\u00e1 na segunda forma normal se cada atributo n\u00e3o-chave depender da chave prim\u00e1ria, eliminando as depend\u00eancias parciais. Para garantir que nosso esquema de pizza esteja em conformidade com a 2NF, precisamos dividir a tabela em duas para eliminar essas depend\u00eancias.<\/p>\n<p><b>Clientes e pedidos<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderID<\/b><\/td>\n<td><b>Nome do cliente<\/b><\/td>\n<td><b>Endere\u00e7o<\/b><\/td>\n<td><b>Telefone<\/b><\/td>\n<td><b>Data de entrega<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123 Elm St.<\/td>\n<td>123-456-7892<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>Alice Johnson<\/td>\n<td>456 Oak St.<\/td>\n<td>987-654-3211<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Detalhes da pizza<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<th><b>PizzaID<\/b><\/th>\n<th><b>OrderID<\/b><\/th>\n<th><b>PizzaType<\/b><\/th>\n<th><b>Tamanho<\/b><\/th>\n<th><b>Pre\u00e7o<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>Pepperoni<\/td>\n<td>Grande<\/td>\n<td>15.00<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>1<\/td>\n<td>Margherita<\/td>\n<td>M\u00e9dio<\/td>\n<td>10.00<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>3<\/td>\n<td>2<\/td>\n<td>Vegetariano<\/td>\n<td>Pequeno<\/td>\n<td>12.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>Terceira forma normal (3NF)<\/h4>\n<p>Diz-se que uma tabela est\u00e1 na terceira forma normal se j\u00e1 estiver na 2NF, e todos os atributos que n\u00e3o s\u00e3o chaves dependem apenas da chave prim\u00e1ria, e n\u00e3o de outros atributos que n\u00e3o s\u00e3o chaves. A 3NF elimina as rela\u00e7\u00f5es indiretas para garantir que a tabela seja bem estruturada e evite redund\u00e2ncia.<\/p>\n<p>Para converter nosso esquema de pizza para a terceira forma normal, precisamos:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Mova os dados reutiliz\u00e1veis para tabelas separadas.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Criar um <b>Tipos de pizza<\/b> tabela para informa\u00e7\u00f5es sobre pizza, como tipo e tamanho.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><b>Clientes e pedidos<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderID<\/b><\/td>\n<td><b>ID do cliente<\/b><\/td>\n<td><b>Data de entrega<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>2<\/td>\n<td>2025-01-15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Clientes<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>ID do cliente<\/b><\/td>\n<td><b>Nome do cliente<\/b><\/td>\n<td><b>Endere\u00e7o<\/b><\/td>\n<td><b>Telefone<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123 Elm St.<\/td>\n<td>123-456-7892<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>Alice Johnson<\/td>\n<td>456 Oak St.<\/td>\n<td>987-654-3211<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Detalhes do pedido<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderDetailID<\/b><\/td>\n<td><b>OrderID<\/b><\/td>\n<td><b>PizzaID<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>1<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>1<\/td>\n<td>2<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>3<\/td>\n<td>2<\/td>\n<td>3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Tipos de pizza<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>PizzaID<\/b><\/td>\n<td><b>PizzaType<\/b><\/td>\n<td><b>Tamanho<\/b><\/td>\n<td><b>Pre\u00e7o<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>Pepperoni<\/td>\n<td>Grande<\/td>\n<td>15.00<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>2<\/td>\n<td>Margherita<\/td>\n<td>M\u00e9dio<\/td>\n<td>10.00<\/td>\n<\/tr>\n<tr style=\"background-color: transparent!important;\">\n<td>3<\/td>\n<td>Vegetariano<\/td>\n<td>Pequeno<\/td>\n<td>12.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>O <a href=\"https:\/\/en.wikipedia.org\/wiki\/Third_normal_form#%22Nothing_but_the_key%22\">Juramento 3NF<\/a> tamb\u00e9m \u00e9 um bom resumo das tr\u00eas primeiras formas normais:<\/p>\n<p style=\"text-align: center;\"><i>\"Todo atributo que n\u00e3o seja da chave deve fornecer um fato sobre a chave, toda a chave e nada al\u00e9m da chave, ent\u00e3o me ajude Codd.\"<\/i><\/p>\n<h2>Como desnormalizar dados<\/h2>\n<p>A desnormaliza\u00e7\u00e3o de dados normalmente envolve a duplica\u00e7\u00e3o de dados em v\u00e1rias tabelas ou bancos de dados. Essa abordagem pode ser aplicada por:<\/p>\n<ol>\n<li><b> Identifica\u00e7\u00e3o de gargalos de desempenho<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Analisar consultas lentas que envolvem v\u00e1rias uni\u00f5es.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Identificar tabelas frequentemente unidas em opera\u00e7\u00f5es de leitura pesada.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Use ferramentas de cria\u00e7\u00e3o de perfil de consulta para medir o impacto no desempenho.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ol start=\"2\">\n<li><b> Adi\u00e7\u00e3o de dados redundantes<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Duplique colunas acessadas com frequ\u00eancia de tabelas relacionadas em uma \u00fanica tabela.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Armazenar valores computados (por exemplo, total de vendas e \u00faltimo login) em vez de calcul\u00e1-los em tempo real.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ol start=\"3\">\n<li><b> Mesclagem de tabelas (dados pr\u00e9-uni\u00e3o)<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Combine tabelas normalizadas que s\u00e3o unidas com frequ\u00eancia para reduzir os tempos de pesquisa.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Exemplo:<\/b> Em vez de armazenar <b>Clientes<\/b> e <b>Detalhes do pedido<\/b> separadamente e uni-los, armazenar os dados do cliente diretamente no <b>Detalhes do pedido<\/b> mesa.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ol start=\"4\">\n<li><b> Cria\u00e7\u00e3o de tabelas agregadas<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Armazene resumos ou rollups pr\u00e9-computados para evitar c\u00e1lculos dispendiosos.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Exemplo:<\/b> Em vez de calcular dinamicamente o total de vendas por m\u00eas, mantenha um <b>Vendas mensais<\/b> mesa.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ol start=\"5\">\n<li><b> Uso de armazenamentos de valores-chave ou de documentos<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Se voc\u00ea estiver <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/resources\/why-nosql\/\">usando um banco de dados NoSQL<\/a>incorporam dados relacionados em um \u00fanico documento em vez de normaliz\u00e1-los em cole\u00e7\u00f5es separadas.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ol start=\"6\">\n<li><b> Desnormaliza\u00e7\u00e3o seletiva<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Equil\u00edbrio entre redund\u00e2ncia e capacidade de manuten\u00e7\u00e3o.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Somente desnormalize quando os ganhos de desempenho justificarem a poss\u00edvel complexidade.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ol start=\"7\">\n<li><b> Implementa\u00e7\u00e3o de acionadores ou atualiza\u00e7\u00f5es em lote<\/b><\/li>\n<\/ol>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Uso <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/resources\/concepts\/batch-processing\/\">acionadores de banco de dados ou trabalhos agendados<\/a> para manter os dados desnormalizados atualizados quando os dados de origem forem alterados.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>Exemplo de desnormaliza\u00e7\u00e3o<\/h3>\n<h4>Antes (dados normalizados)<\/h4>\n<p><b>Clientes<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>ID do cliente<\/b><\/td>\n<td><b>Nome do cliente<\/b><\/td>\n<td><b>Telefone<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123-456-7892<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Detalhes do pedido<\/b> mesa:<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderID<\/b><\/td>\n<td><b>ID do cliente<\/b><\/td>\n<td><b>Pre\u00e7o<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>25.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>Depois (dados desnormalizados)<\/h4>\n<p><b>Detalhes do pedido<\/b> tabela (inclui dados do cliente):<\/p>\n<table style=\"border: 1px solid Gainsboro;\">\n<thead>\n<tr style=\"background-color: transparent!important;\">\n<td><b>OrderID<\/b><\/td>\n<td><b>ID do cliente<\/b><\/td>\n<td><b>Nome do cliente<\/b><\/td>\n<td><b>Telefone<\/b><\/td>\n<td><b>Pre\u00e7o<\/b><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color: transparent!important;\">\n<td>1<\/td>\n<td>1<\/td>\n<td>John Smith<\/td>\n<td>123-456-7892<\/td>\n<td>$25.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Isso elimina a necessidade de uma uni\u00e3o ao recuperar as informa\u00e7\u00f5es do cliente com seus pedidos.<\/p>\n<h2>Casos de uso<\/h2>\n<h3>Normaliza\u00e7\u00e3o de dados<\/h3>\n<p><b>Sistemas banc\u00e1rios:<\/b><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado para armazenar contas de clientes, transa\u00e7\u00f5es e detalhes de empr\u00e9stimos para manter a precis\u00e3o.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Os sistemas banc\u00e1rios usam a normaliza\u00e7\u00e3o para evitar a duplica\u00e7\u00e3o de dados financeiros confidenciais e garantir que as atualiza\u00e7\u00f5es (por exemplo, altera\u00e7\u00e3o de endere\u00e7os de clientes) sejam propagadas corretamente.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><b>Sistemas de sa\u00fade:<\/b><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado para armazenar dados de pacientes, registros m\u00e9dicos e consultas em tabelas separadas.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Os sistemas de sa\u00fade dependem da normaliza\u00e7\u00e3o para reduzir a duplica\u00e7\u00e3o de dados e garantir informa\u00e7\u00f5es precisas sobre os pacientes, o que \u00e9 essencial para manter a conformidade com a HIPAA.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><b>Aplicativos de com\u00e9rcio eletr\u00f4nico:<\/b><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado para armazenar produtos, pedidos, clientes e estoque.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Os aplicativos de com\u00e9rcio eletr\u00f4nico usam a normaliza\u00e7\u00e3o para garantir o rastreamento correto do estoque e a precis\u00e3o dos pedidos dos clientes.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>Desnormaliza\u00e7\u00e3o de dados<\/h3>\n<p><b>Dados <\/b><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/database-vs-data-warehouse\/\"><b>Armazenamento<\/b><\/a>:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado em sistemas de business intelligence para consolidar dados de vendas, marketing e opera\u00e7\u00f5es.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Os data warehouses usam a desnormaliza\u00e7\u00e3o para permitir a gera\u00e7\u00e3o mais r\u00e1pida de relat\u00f3rios e a renderiza\u00e7\u00e3o de pain\u00e9is, pois os dados agregados reduzem a necessidade de jun\u00e7\u00f5es em tempo de execu\u00e7\u00e3o.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><b>Plataformas de m\u00eddia social<\/b>:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado para armazenar publica\u00e7\u00f5es, curtidas, coment\u00e1rios e dados de seguidores do usu\u00e1rio.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">As plataformas de m\u00eddia social usam a desnormaliza\u00e7\u00e3o para melhorar o desempenho da gera\u00e7\u00e3o de feeds, armazenando dados pr\u00e9-agregados ou redundantes, reduzindo a complexidade da consulta.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><b>Sistemas de distribui\u00e7\u00e3o de conte\u00fado (CDNs)<\/b>:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado por servi\u00e7os de streaming para armazenar metadados de v\u00eddeo e hist\u00f3rico de visualiza\u00e7\u00e3o do usu\u00e1rio.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">As CDNs dependem da desnormaliza\u00e7\u00e3o para garantir a recupera\u00e7\u00e3o r\u00e1pida das recomenda\u00e7\u00f5es e do hist\u00f3rico de exibi\u00e7\u00e3o <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/use-cases\/media-and-entertainment\/\">durante os hor\u00e1rios de pico de carga<\/a>.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><b>Aplicativos de jogos<\/b>:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Usado para armazenar perfis de jogadores, conquistas e eventos no jogo em estruturas simplificadas.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/use-cases\/gaming\/\">Sistemas de jogos<\/a> usar a desnormaliza\u00e7\u00e3o para recupera\u00e7\u00e3o de dados de baixa lat\u00eancia para aprimorar as experi\u00eancias de jogo em tempo real.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Benef\u00edcios e desafios\u00a0<\/h2>\n<h3>Benef\u00edcios da normaliza\u00e7\u00e3o de dados<\/h3>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Elimina a redund\u00e2ncia de dados:<\/b> A normaliza\u00e7\u00e3o reduz a duplica\u00e7\u00e3o de dados, levando a um uso eficiente do armazenamento e minimizando as inconsist\u00eancias.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Garante a integridade e a consist\u00eancia dos dados:<\/b> Como os dados s\u00e3o armazenados de forma estruturada, com relacionamentos mantidos por meio de chaves, as atualiza\u00e7\u00f5es e exclus\u00f5es s\u00e3o mais consistentes em todo o banco de dados.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Reduz as anomalias de atualiza\u00e7\u00e3o:<\/b> Com menos dados redundantes, as altera\u00e7\u00f5es (como a atualiza\u00e7\u00e3o de um endere\u00e7o) s\u00f3 precisam ser feitas em um \u00fanico local, reduzindo o risco de atualiza\u00e7\u00f5es inconsistentes.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Aumenta a seguran\u00e7a dos dados:<\/b> As informa\u00e7\u00f5es confidenciais podem ser armazenadas em tabelas separadas com acesso restrito, melhorando as medidas de seguran\u00e7a.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Melhora a capacidade de manuten\u00e7\u00e3o:<\/b> Um banco de dados bem estruturado \u00e9 mais f\u00e1cil de ser modificado, ampliado e escalonado ao longo do tempo, pois as altera\u00e7\u00f5es s\u00e3o localizadas em tabelas espec\u00edficas em vez de serem espalhadas por dados redundantes.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Otimiza o desempenho transacional:<\/b> Os bancos de dados normalizados garantem o tratamento eficiente de transa\u00e7\u00f5es para sistemas que exigem inser\u00e7\u00f5es, atualiza\u00e7\u00f5es e exclus\u00f5es frequentes (sistemas OLTP).<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Refor\u00e7a a integridade referencial:<\/b> As chaves estrangeiras garantem que os relacionamentos entre as tabelas sejam mantidos corretamente, evitando registros \u00f3rf\u00e3os ou inv\u00e1lidos.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>Desafios da normaliza\u00e7\u00e3o de dados<\/h3>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Consultas de leitura lentas:<\/b> Os bancos de dados normalizados geralmente exigem v\u00e1rias uni\u00f5es para recuperar dados relacionados, o que reduz o desempenho, especialmente para aplicativos de leitura intensa.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Aumento da complexidade da consulta:<\/b> A recupera\u00e7\u00e3o de dados requer a cria\u00e7\u00e3o de consultas SQL complexas com v\u00e1rias jun\u00e7\u00f5es.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Maior uso da CPU e da mem\u00f3ria:<\/b> Cada opera\u00e7\u00e3o de uni\u00e3o requer processamento adicional, o que pode sobrecarregar os recursos do banco de dados.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mais dif\u00edcil de dimensionar horizontalmente:<\/b> Os bancos de dados normalizados funcionam bem em ambientes de n\u00f3 \u00fanico, mas podem n\u00e3o ser dimensionados de forma eficiente em ambientes de n\u00f3 \u00fanico. <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/distributed-applications\/\">arquiteturas distribu\u00eddas<\/a>.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Aumento do esfor\u00e7o de desenvolvimento e manuten\u00e7\u00e3o:<\/b> O projeto de um esquema bem normalizado requer planejamento cuidadoso e conhecimento especializado (especialmente para aplicativos maiores).<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Desafios de relat\u00f3rios e an\u00e1lises: <\/b>Os bancos de dados normalizados n\u00e3o s\u00e3o ideais para sistemas de processamento anal\u00edtico on-line (OLAP), pois a agrega\u00e7\u00e3o de dados para relat\u00f3rios geralmente requer jun\u00e7\u00f5es e c\u00e1lculos caros.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Restri\u00e7\u00f5es de integridade de dados: <\/b>A imposi\u00e7\u00e3o de restri\u00e7\u00f5es de chave estrangeira e regras de normaliza\u00e7\u00e3o pode, \u00e0s vezes, tornar as inser\u00e7\u00f5es e atualiza\u00e7\u00f5es em massa mais lentas.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>Benef\u00edcios da desnormaliza\u00e7\u00e3o de dados<\/h3>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Melhoria do desempenho de leitura:<\/b> A desnormaliza\u00e7\u00e3o acelera as consultas de leitura ao reduzir o n\u00famero de uni\u00f5es necess\u00e1rias para recuperar dados, o que \u00e9 especialmente \u00fatil em aplicativos de leitura intensa.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Redu\u00e7\u00e3o da complexidade da consulta:<\/b> Como os dados s\u00e3o pr\u00e9-unidos ou duplicados, as consultas se tornam mais simples, o que facilita para os desenvolvedores escreverem e manterem as consultas.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Relat\u00f3rios e agrega\u00e7\u00e3o mais r\u00e1pidos:<\/b> As estruturas desnormalizadas s\u00e3o adequadas para tarefas anal\u00edticas e de gera\u00e7\u00e3o de relat\u00f3rios, em que os dados precisam ser acessados rapidamente em massa.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Melhor desempenho do cache:<\/b> Ao armazenar dados redundantes em um \u00fanico documento ou tabela, <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/caching-comparison\/\">caches<\/a> pode atender com mais efici\u00eancia a registros completos e reduzir a carga do banco de dados.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Abaixe a junta acima da cabe\u00e7a:<\/b> A desnormaliza\u00e7\u00e3o minimiza o uso da CPU e da mem\u00f3ria, reduzindo a necessidade de jun\u00e7\u00f5es.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Escalabilidade:<\/b> Os dados desnormalizados geralmente s\u00e3o mais adequados para bancos de dados distribu\u00eddos (por exemplo, bancos de dados NoSQL) que priorizam <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/resources\/concepts\/database-scalability\/\">dimensionamento horizontal<\/a> e recupera\u00e7\u00e3o r\u00e1pida de dados.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Indexa\u00e7\u00e3o mais r\u00e1pida:<\/b> Como os dados relacionados s\u00e3o armazenados juntos, os \u00edndices podem ser mais eficazes, levando a pesquisas mais r\u00e1pidas.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>Desafios da desnormaliza\u00e7\u00e3o de dados<\/h3>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Aumento da redund\u00e2ncia de dados:<\/b> Os dados s\u00e3o duplicados em v\u00e1rias tabelas ou documentos, o que leva a maiores requisitos de armazenamento.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Riscos de inconsist\u00eancia de dados:<\/b> Como a desnormaliza\u00e7\u00e3o envolve dados redundantes, \u00e9 fundamental lembrar-se de aplicar as altera\u00e7\u00f5es em todas as c\u00f3pias de forma consistente.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Maior sobrecarga de atualiza\u00e7\u00e3o e exclus\u00e3o:<\/b> A atualiza\u00e7\u00e3o ou exclus\u00e3o de registros exige a modifica\u00e7\u00e3o de v\u00e1rias inst\u00e2ncias dos mesmos dados.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mais dif\u00edcil de manter e dimensionar:<\/b> As altera\u00e7\u00f5es de esquema exigem a atualiza\u00e7\u00e3o de v\u00e1rios locais, aumentando o esfor\u00e7o de manuten\u00e7\u00e3o.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Opera\u00e7\u00f5es de grava\u00e7\u00e3o mais complexas:<\/b> As grava\u00e7\u00f5es se tornam mais caras porque as atualiza\u00e7\u00f5es precisam ser aplicadas a v\u00e1rios locais.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Aumento dos custos de armazenamento:<\/b> O armazenamento de dados redundantes leva a um maior uso do disco, o que pode ser caro para aplicativos grandes.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mais dif\u00edcil de impor restri\u00e7\u00f5es:<\/b> A desnormaliza\u00e7\u00e3o geralmente reduz o uso de chaves estrangeiras, tornando a integridade referencial mais dif\u00edcil de ser aplicada.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Principais conclus\u00f5es e recursos<\/h2>\n<p>O uso da normaliza\u00e7\u00e3o ou desnormaliza\u00e7\u00e3o de dados depende das necessidades espec\u00edficas de seu aplicativo. A normaliza\u00e7\u00e3o garante a integridade, a consist\u00eancia e a efici\u00eancia das atualiza\u00e7\u00f5es dos dados, o que a torna ideal para sistemas transacionais (OLTP) em que a minimiza\u00e7\u00e3o da redund\u00e2ncia \u00e9 crucial. Por outro lado, a desnormaliza\u00e7\u00e3o melhora o desempenho da consulta e a efici\u00eancia da leitura, tornando-a adequada para cargas de trabalho anal\u00edticas (OLAP), relat\u00f3rios e bancos de dados NoSQL. Em \u00faltima an\u00e1lise, compreender sua <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/resources\/concepts\/types-of-databases\/\">padr\u00f5es de uso do banco de dados<\/a>A melhor maneira de determinar a melhor abordagem para o seu sistema \u00e9 avaliar as necessidades de escalabilidade e as metas de desempenho.<\/p>\n<p>Para saber mais sobre gerenciamento de dados, confira os recursos abaixo:<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/denormalize-imported-documents-rdbms-to-couchbase-using-n1ql\/\">Como desnormalizar documentos importados de tabelas RDBMS usando o SQL++<\/a><\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/resources\/concepts\/write-back-cache\/\">Cache de write-back - Conceitos<\/a><\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/resources\/concepts\/what-is-data-management\/\">O que \u00e9 gerenciamento de dados? - Conceitos<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><br style=\"font-weight: 400;\" \/><br style=\"font-weight: 400;\" \/><\/p>","protected":false},"excerpt":{"rendered":"<p>What is data normalization? Data normalization is the process of structuring a database to enhance efficiency, maintain consistency, and eliminate redundant data. The idea is simple: break data into smaller, related tables to minimize repetition and simplify updates. By following [&hellip;]<\/p>\n","protected":false},"author":77912,"featured_media":16934,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1814,1815],"tags":[1447,9467,1450],"ppma_author":[9311],"class_list":["post-16931","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-best-practices-and-tutorials","tag-data-modeling","tag-denormalization","tag-document-normalization"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Data Normalization vs. Denormalization Comparison - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"Learn the primary differences between data normalization vs. denormalization, along with their benefits, challenges, and use cases.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/pt\/normalization-vs-denormalization\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Normalization vs. Denormalization Comparison\" \/>\n<meta property=\"og:description\" content=\"Learn the primary differences between data normalization vs. denormalization, along with their benefits, challenges, and use cases.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/pt\/normalization-vs-denormalization\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2025-02-28T20:42:35+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/02\/what-is-data-normalization-denormalization.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2400\" \/>\n\t<meta property=\"og:image:height\" content=\"1256\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Tim Rottach, Director of Product Line Marketing\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Tim Rottach, Director of Product Line Marketing\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/\"},\"author\":{\"name\":\"Tim Rottach, Director of Product Line Marketing\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#\\\/schema\\\/person\\\/02596c1f54a5dd8d2094d919487485cc\"},\"headline\":\"Data Normalization vs. Denormalization Comparison\",\"datePublished\":\"2025-02-28T20:42:35+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/\"},\"wordCount\":2014,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2025\\\/02\\\/what-is-data-normalization-denormalization.png\",\"keywords\":[\"Data Modeling\",\"denormalization\",\"Document Normalization\"],\"articleSection\":[\"Application Design\",\"Best Practices and Tutorials\"],\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/\",\"name\":\"Data Normalization vs. Denormalization Comparison - The Couchbase Blog\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2025\\\/02\\\/what-is-data-normalization-denormalization.png\",\"datePublished\":\"2025-02-28T20:42:35+00:00\",\"description\":\"Learn the primary differences between data normalization vs. denormalization, along with their benefits, challenges, and use cases.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2025\\\/02\\\/what-is-data-normalization-denormalization.png\",\"contentUrl\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2025\\\/02\\\/what-is-data-normalization-denormalization.png\",\"width\":2400,\"height\":1256,\"caption\":\"What is data normalization denormalization\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/normalization-vs-denormalization\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Data Normalization vs. Denormalization Comparison\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/\",\"name\":\"The Couchbase Blog\",\"description\":\"Couchbase, the NoSQL Database\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#organization\",\"name\":\"The Couchbase Blog\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/2023\\\/04\\\/admin-logo.png\",\"contentUrl\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/2023\\\/04\\\/admin-logo.png\",\"width\":218,\"height\":34,\"caption\":\"The Couchbase Blog\"},\"image\":{\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#\\\/schema\\\/logo\\\/image\\\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/#\\\/schema\\\/person\\\/02596c1f54a5dd8d2094d919487485cc\",\"name\":\"Tim Rottach, Director of Product Line Marketing\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2021\\\/07\\\/timothy-rottach-couchbase.jpeg93228766273ae64ba068eecec5523b48\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2021\\\/07\\\/timothy-rottach-couchbase.jpeg\",\"contentUrl\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/wp-content\\\/uploads\\\/sites\\\/1\\\/2021\\\/07\\\/timothy-rottach-couchbase.jpeg\",\"caption\":\"Tim Rottach, Director of Product Line Marketing\"},\"description\":\"Tim Rottach is Director of Product Line Marketing at Couchbase.\",\"url\":\"https:\\\/\\\/www.couchbase.com\\\/blog\\\/pt\\\/author\\\/timothy-rottach\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Data Normalization vs. Denormalization Comparison - The Couchbase Blog","description":"Conhe\u00e7a as principais diferen\u00e7as entre normaliza\u00e7\u00e3o e desnormaliza\u00e7\u00e3o de dados, al\u00e9m de seus benef\u00edcios, desafios e casos de uso.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.couchbase.com\/blog\/pt\/normalization-vs-denormalization\/","og_locale":"pt_BR","og_type":"article","og_title":"Data Normalization vs. Denormalization Comparison","og_description":"Learn the primary differences between data normalization vs. denormalization, along with their benefits, challenges, and use cases.","og_url":"https:\/\/www.couchbase.com\/blog\/pt\/normalization-vs-denormalization\/","og_site_name":"The Couchbase Blog","article_published_time":"2025-02-28T20:42:35+00:00","og_image":[{"width":2400,"height":1256,"url":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/02\/what-is-data-normalization-denormalization.png","type":"image\/png"}],"author":"Tim Rottach, Director of Product Line Marketing","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Tim Rottach, Director of Product Line Marketing","Est. reading time":"10 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#article","isPartOf":{"@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/"},"author":{"name":"Tim Rottach, Director of Product Line Marketing","@id":"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/02596c1f54a5dd8d2094d919487485cc"},"headline":"Data Normalization vs. Denormalization Comparison","datePublished":"2025-02-28T20:42:35+00:00","mainEntityOfPage":{"@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/"},"wordCount":2014,"commentCount":0,"publisher":{"@id":"https:\/\/www.couchbase.com\/blog\/#organization"},"image":{"@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#primaryimage"},"thumbnailUrl":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/02\/what-is-data-normalization-denormalization.png","keywords":["Data Modeling","denormalization","Document Normalization"],"articleSection":["Application Design","Best Practices and Tutorials"],"inLanguage":"pt-BR","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/","url":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/","name":"Data Normalization vs. Denormalization Comparison - The Couchbase Blog","isPartOf":{"@id":"https:\/\/www.couchbase.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#primaryimage"},"image":{"@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#primaryimage"},"thumbnailUrl":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/02\/what-is-data-normalization-denormalization.png","datePublished":"2025-02-28T20:42:35+00:00","description":"Conhe\u00e7a as principais diferen\u00e7as entre normaliza\u00e7\u00e3o e desnormaliza\u00e7\u00e3o de dados, al\u00e9m de seus benef\u00edcios, desafios e casos de uso.","breadcrumb":{"@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/"]}]},{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#primaryimage","url":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/02\/what-is-data-normalization-denormalization.png","contentUrl":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/02\/what-is-data-normalization-denormalization.png","width":2400,"height":1256,"caption":"What is data normalization denormalization"},{"@type":"BreadcrumbList","@id":"https:\/\/www.couchbase.com\/blog\/normalization-vs-denormalization\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.couchbase.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Data Normalization vs. Denormalization Comparison"}]},{"@type":"WebSite","@id":"https:\/\/www.couchbase.com\/blog\/#website","url":"https:\/\/www.couchbase.com\/blog\/","name":"Blog do Couchbase","description":"Couchbase, o banco de dados NoSQL","publisher":{"@id":"https:\/\/www.couchbase.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.couchbase.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/www.couchbase.com\/blog\/#organization","name":"Blog do Couchbase","url":"https:\/\/www.couchbase.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/www.couchbase.com\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/04\/admin-logo.png","contentUrl":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2023\/04\/admin-logo.png","width":218,"height":34,"caption":"The Couchbase Blog"},"image":{"@id":"https:\/\/www.couchbase.com\/blog\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/02596c1f54a5dd8d2094d919487485cc","name":"Tim Rottach, diretor de marketing de linha de produtos","image":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/07\/timothy-rottach-couchbase.jpeg93228766273ae64ba068eecec5523b48","url":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/07\/timothy-rottach-couchbase.jpeg","contentUrl":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/07\/timothy-rottach-couchbase.jpeg","caption":"Tim Rottach, Director of Product Line Marketing"},"description":"Tim Rottach \u00e9 diretor de marketing da linha de produtos da Couchbase.","url":"https:\/\/www.couchbase.com\/blog\/pt\/author\/timothy-rottach\/"}]}},"acf":[],"authors":[{"term_id":9311,"user_id":77912,"is_guest":0,"slug":"timothy-rottach","display_name":"Tim Rottach, Director of Product Line Marketing","avatar_url":{"url":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/07\/timothy-rottach-couchbase.jpeg","url2x":"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/07\/timothy-rottach-couchbase.jpeg"},"0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/posts\/16931","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/users\/77912"}],"replies":[{"embeddable":true,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/comments?post=16931"}],"version-history":[{"count":0,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/posts\/16931\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/media\/16934"}],"wp:attachment":[{"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/media?parent=16931"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/categories?post=16931"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/tags?post=16931"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.couchbase.com\/blog\/pt\/wp-json\/wp\/v2\/ppma_author?post=16931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}