Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
OLAP (Online Analytical Processing) is a database workload pattern characterised by complex analytical queries — aggregations, filters, joins, and slice-and-dice operations across large historical data sets — typically used for business intelligence, reporting, and data analytics use cases.
OLAP is one of the two foundational database workload categories in computing, the other being OLTP (Online Transactional Processing). The distinction matters because the database architecture, indexing, storage format, and hardware optimisations are very different for each.
Modern data warehouses like Snowflake, BigQuery, and Databricks SQL are purpose-built for OLAP workloads. They use columnar storage, massively parallel processing, and elastic compute to answer analytical queries in seconds even on multi-terabyte data sets — something traditional OLTP databases like Postgres or MySQL struggle to do efficiently.
OLAP workloads have distinct characteristics:
Traditional OLAP systems pre-computed aggregations into “OLAP cubes” — multi-dimensional data structures optimised for slice-and-dice. Modern cloud warehouses largely make OLAP cubes redundant by being fast enough to compute aggregations on demand, though specialised OLAP engines like Apache Druid and ClickHouse still excel at sub-second OLAP queries.
A finance analyst opens a BI dashboard that shows revenue broken down by product, region, and customer segment, comparing this quarter to last quarter. The underlying SQL aggregates 50 million invoice rows, joins to a customer dimension, and pivots by product. On a Postgres OLTP database this query might take 5 minutes; on a Snowflake OLAP warehouse it returns in 3 seconds because of columnar storage and parallel compute. This is OLAP in action.
Modern OLAP engines and warehouses in 2026:
Cloud OLAP warehouse with separate compute/storage. Industry leader for mid-market and enterprise.
Google Cloud OLAP warehouse. Serverless, pay-per-query.
Lakehouse-style OLAP with strong ML integration.
Open-source OLAP database with sub-second query latency on huge data sets.
Open-source real-time OLAP for streaming data.
Modern open-source OLAP engines popular in 2026.
OLAP is for analytical queries — aggregations, reports, dashboards. OLTP is for transactional queries — inserts, updates, single-record reads. OLAP databases use columnar storage; OLTP databases use row-based storage.
Less frequently. Modern cloud warehouses (Snowflake, BigQuery) are fast enough to compute aggregations on the fly without pre-computed cubes. Cubes persist in legacy SAP BW deployments and in specialised tools like Microsoft Analysis Services.
A data warehouse is a database optimised for OLAP workloads. The terms are often used interchangeably, though “data warehouse” implies the broader system (ETL pipelines, governance) and “OLAP” implies the workload pattern.
ROLAP (Relational OLAP) computes aggregations from relational tables on demand. MOLAP (Multi-dimensional OLAP) pre-computes aggregations into cubes. HOLAP (Hybrid OLAP) mixes both. Modern cloud warehouses are largely ROLAP.
For small data sets (under 100GB) yes. For larger workloads Postgres struggles because it stores data row-by-row. Use Postgres for OLTP and a separate cloud warehouse for OLAP, with ELT pipelines syncing between them.
Less than OLTP queries. Columnar OLAP databases use compression and zone maps instead of B-tree indexes. Sort keys and clustering keys are used to optimise common query patterns.