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Analytical processing

Interactive processing of data through complex queries, transformations, and aggregations to support analysis, reporting, or decision-making.

How 2 Manning books explain it

Cloud Observability in Action

Online analytical processing is a workload pattern involving complex queries over large volumes of historical data, typically with fewer writes than transactional systems.

How it is used
Supports interactive insight extraction from large historical datasets, especially observability data, with relatively few transactions.
How deep it goes
Explains row- and column-oriented layouts and workload fit, but does not develop deeper storage-engine internals.
Examples
Uses ClickHouse, OpenTelemetry logs, Docker Compose, and a time-bounded SQL query as working artifacts.
What you can do after
Operate/configure ClickHouse with an OpenTelemetry Collector pipeline and query recent logs for matching events.

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DuckDB in Action

Analytical processing is computation over data to produce aggregates, transformations, and insights rather than to support operational transactions.

How it is used
Deploys computation for local analytics, preprocessing, reporting, forecasting, and aggregation across device, edge, cloud, and notebook workflows.
How deep it goes
Explains practical SQL workflows through ingestion prerequisites, basic statements, common table expressions, window functions, and OLAP queries.
Examples
Uses log files, sensor readings, and PVDAQ quarter-hour photovoltaic measurements to demonstrate reduction, forecasting, and reporting.
What you can do after
Apply it via DuckDB to prepare, filter, aggregate, query, and report on larger datasets across local and cloud workflows.

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Related concepts

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