How do you tune data queries for different data sources and platforms, such as SQL, NoSQL, or cloud services?

SQL Basics ??: SQL (Structured Query Language) is the standard language for querying relational databases, which store data in predefined table schemas. ??? Indexing ??: Indexing creates data structures that improve query speed by allowing quicker access to specific rows or columns. ?? Partitioning ???: Partitioning divides large tables into smaller, more manageable segments based on criteria like date or region, enhancing performance. ?? Joins ??: Joins combine data from multiple tables based on a common attribute, allowing for comprehensive data retrieval. ?? Filtering ?? Filtering reduces the result set by applying conditions to limit the data returned by a query. ?? Aggregation ??:

Know the Data Structure: In SQL, I focus on joins and indexes. For NoSQL, I keep queries simple and keyed to primary fields. Use Platform Tools: I rely on tools like EXPLAIN in SQL or NoSQL’s Query Profiler to find slow spots, and tools like BigQuery’s Query Insights in the cloud. Optimize Storage: Index high-traffic fields and use partitions. In the cloud, clustering by common fields cuts scan costs. Batch and Cache: I batch heavy workloads and cache frequent queries. Monitor Regularly: I track performance over time and adjust as needed.

SQL Optimization Use indexing, partitioning, and clustering for faster retrieval. Avoid SELECT *, fetch only required columns. Optimize joins by selecting appropriate types (INNER, LEFT). Use query execution plans to identify bottlenecks. ?? NoSQL Optimization Design schema based on access patterns. Use efficient indexing and aggregation pipelines. Optimize queries with proper filtering and projection. ?? Cloud Services Use caching (Redis, CDN) for frequent queries. Optimize storage costs with partitioning & auto-scaling. Leverage parallel processing (BigQuery, Snowflake) for heavy workloads. ??

Algumas a??es típicas que podem ajudar numa consulta ágil, Consulta NoSQL (MongoDB) Ao invés de pensarmos em modo tabelar, devemos pensar que temos um conjunto de documento em formato lista, por exemplo: { “nome”: “Joana” “altura”: 156 “idade”: 22 } Se quiséssemos encontrar todas as pessoas com 22 anos, db.pessoas.find ({ idade : 22 }, { nome: 1 }); find() - fun??o para procurar { idade: 22 } - filtra pessoas com idade =22 { nome: 1 } - apenas queremos o output nome Aqui n?o precisamos de uma estrutura rígida de dados, existe mais flexibilidade no entanto a consulta pode tornar-se mais complexa.