Crossquerying

Crossquerying refers to the practice of executing queries that span multiple data sources or datasets in order to produce an integrated view. It is used when data relevant to a question is distributed across different systems, such as relational databases, data warehouses, NoSQL stores, file systems, or application APIs. In crossquerying, a single logical query is typically broken into subqueries against each source, and the results are merged to form a unified result set.

The core technique is query federation or federated querying, often supported by data virtualization layers or distributed query engines. Federation relies on adapters or connectors to each source and sometimes on schema mapping or harmonization to enable meaningful joins or associations across datasets. In the semantic web, federated querying is exemplified by SPARQL queries that pull data from multiple endpoints using SERVICE blocks.

Architectures for crossquerying commonly include a federated query engine or data virtualization layer that sits between users and the data sources, plus source-specific connectors and a component that merges subresults. This setup can support real-time or near-real-time analysis without duplicating all data into a central repository, though some implementations also incorporate caching or indexing to improve performance.

Use cases include cross-department business intelligence, research that combines heterogeneous data such as clinical and operational datasets, and analytics across partner or external data stores. Benefits include avoiding data duplication and enabling up-to-date insights, while challenges cover latency, partial failures, schema heterogeneity, security and governance, and the complexity of query planning and optimization.

See also: federated databases, data virtualization, data integration, ETL/ELT, schema matching.