Wie wirkt sich die Normalisierung auf die Komplexität von Datenbankabfragen aus?

Database normalisation impacts the speed of queries. Normalisation ensures that the keys are referred to in the joins. Too much normalised data slows down database queries for reports.

A normalização tem como vantagem a eliminação de duplicidade de atributos e a ocupação de espaço em disco, visto que atributos que possuem os mesmos valores e que não compõem a chave primária, possuam dependência transitiva de outros atributos e também, casos de itens multivalores (telefone, por exemplo, pois não sabemos o número exato de colunas necessárias para armazenar todas as composições) são armazenados de um jeito otimizado. Em contrapartida, isso aumenta o número de junções em tabelas. Então é importante a definição correta das chaves dos atributos, manter eles clusterizados e com pouca fragmentação para evitar consultas NESTEED LOOP, que podem degradar drasticamente o desempenho do SGBD.

Normalization organizes data to reduce redundancy and improve integrity. However, it can make database queries more complex and slower due to the need for more joins and interactions with multiple tables. It’s a trade-off between data organization and query performance

A normalização traz como benefícios a inclusão de campos identificadores (chaves) que apontam para um registro único numa tabela. Isso evita problemas de atributos terem mais de um valor, seja por problemas de preenchimento de dados ou outros eventos.

Database normalisation improves data integrity by enforcing proper refers to keys. Data normalisation ensures that clean data is stored in the database without duplicate data

In order to write queries you need to have strong SQL knowledge. You should use appropriate joins like inner and outer joins. Sub queries can also be used to join multiple tables to extract data. Care should be taken while writing sub- queries in order to avoid risk of errors.

A normalização gera várias tabelas, o que pode fazer com que as consultas tenham uma complexidade maior nas suas escritas. Será necessário a inclusão das junções e avaliar o tipo ideal (interna, externa, a esquerda, total...).

While extremely powerful, and in many cases necessary and appropriate, consider the computational cost of join operations on large data sets. Leveraging multiple tools from traditional indexes and views alongside efficient compartmentalization of processing can be critical to the performance of complex queries.

Performance tuning is the most important aspect to consider to speed up the database. Make sure that proper indexes are created on columns in a table to improve database performance. Performance tuning is the major factor in database normalisation.

You can denormalize to a certain extent like a postal address. This will help you simplify the queries but will impact on redundant data saved in the address table. If you below than 1 million records of address than this approch is somewhat acceptable but on more address records writing complex queries and converting them to views is a better trade-off.

Normalization often leads to data being spread across more tables. To retrieve related data that is distributed among these tables, SQL queries need to use join operations. This can increase the complexity of SQL statements, as joins require careful coordination of the keys that link tables together. Normalization can complicate analytical queries, which often require aggregations of large amounts of data. More joins can lead to slower performance in these cases, which is why sometimes data is denormalized or copied into data warehouses optimized for read-heavy operations.