dataquerying

Data querying is the process of requesting data from one or more data stores by specifying criteria using a query language or interface. It covers retrieval, filtering, joining, and transforming data to support analytics, reporting, and application functionality. Queries may target structured databases, semi-structured stores, graph databases, or large-scale data lakes.

Query languages vary by data model: SQL for relational databases; SPARQL for RDF graphs; Cypher for property graphs; XQuery for XML; JSONPath for JSON. Interfaces include SQL editors, APIs, and streaming query engines. Query processing typically involves parsing, validation, optimization, and execution against data stores.

Core operations include selecting attributes, filtering rows, joining datasets, aggregating values, sorting and grouping. Performance depends on indexes, data distribution, statistics, and execution plans. Modern engines use cost-based optimizers, parallel processing, and rule-based optimizations. Tools include database engines, data warehouses, and distributed query engines such as Spark SQL, Presto/Trino, and Hive.

Applications span business intelligence, dashboards, data science workflows, and application feature development. Common challenges include ensuring data quality and freshness, managing schema evolution, handling semi-structured data, achieving low latency, and enforcing access control and governance.