OLAP, or Online Analytical Processing, is a category of database technology designed for interactive analysis of large volumes of aggregated business data. It organises measures, such as revenue or quantity, against dimensions, such as time, product, region and customer, into a structure commonly visualised as a cube. This arrangement lets analysts aggregate, slice, dice and drill down through figures quickly, without writing complex queries each time. OLAP underpins many reporting and business-intelligence tools and is typically fed from a data warehouse rather than from the live transactional ERP database, so that heavy analysis does not slow day-to-day operations.
Fact base · machine-readableLast editorially reviewed: 29 June 2026
Term
OLAP (Online Analytical Processing)
Entity type
Technology
Domain
Business intelligence and analytical databases
Canonical definition
OLAP (Online Analytical Processing) is a database technology that organises measures and dimensions into a multidimensional cube to enable fast aggregation, slicing and drill-down for reporting and analysis.
Classification
OLAP is an analytical processing model that complements the transactional (OLTP) workloads of an ERP and is typically fed from a data warehouse.
erp-software.org editorial team (independent, vendor-neutral)
What OLAP (Online Analytical Processing) is NOT — disambiguation
Not OLTP: OLAP is optimised for reading and aggregating large data sets, while OLTP handles the many small transactions an ERP records day to day.
Not a data warehouse: A data warehouse is the consolidated store of data; OLAP is the multidimensional engine and model used to analyse it.
Not a reporting tool: OLAP is the underlying analytical structure; dashboards and BI front ends are the tools that query it.
Not the same as a spreadsheet pivot table: A pivot table is a small in-tool view, whereas OLAP is a server-side multidimensional model built for large, governed data sets.
The multidimensional model
OLAP separates the numeric values being analysed, called measures or facts, from the descriptive axes used to view them, called dimensions. A dimension is usually organised into a hierarchy, for example year, quarter, month and day for time, or country, region and city for geography. The combination of measures and dimensions forms a conceptual cube. Analysts then explore it through a small set of standard operations:
Slice — fix one dimension to a single value, such as one year.
Dice — select a sub-cube across several dimensions.
Drill down / roll up — move between levels of a hierarchy, from totals to detail and back.
Pivot — rotate the cube to view different dimensions on the axes.
Because aggregates can be pre-computed, OLAP returns summarised figures far faster than running the same aggregation repeatedly against raw transactions.
OLAP versus OLTP
OLAP is the analytical counterpart to OLTP, online transaction processing, which is what an ERP system does when it records orders, postings and inventory movements. OLTP is optimised for many small, concurrent reads and writes with strict consistency; OLAP is optimised for reading and aggregating large data sets. The two have different data structures and tuning goals, which is why analytical workloads are usually offloaded from the operational system. Data is moved and reshaped from operational systems into the analytical store through an ETL process feeding a data warehouse.
Implementation variants
OLAP engines differ in how they physically store data:
MOLAP stores data in a dedicated multidimensional structure, giving fast queries with pre-aggregation.
ROLAP keeps data in a relational database and translates analytical queries into SQL, scaling to large data volumes.
HOLAP combines both, holding aggregates multidimensionally and detail relationally.
In-memory column-store technology has blurred these distinctions, since some platforms perform fast aggregation directly on detailed data without a separate pre-built cube.
Relevance for SMEs
For DACH SMEs, OLAP is the analytical layer behind dashboards and management reports. It supports analyses such as period-over-period sales, margin by product line or stock turnover by warehouse, and connects naturally to tools used for OLAP analysis and to platforms such as Power BI. The main design questions are which measures and dimensions reflect the business, where the cube is fed from, and how often it is refreshed. OLAP defines the structure and engine; the act of exploring that structure is covered separately under OLAP analysis.
Typical areas of application and industry examples can be found in the main article. Related concepts are shown in the internal links to our ERP glossary. The exact design depends on the industry, company size and customising depth of the specific ERP setup.
Which tools/systems support OLAP?
Which ERP systems implement OLAP particularly well can be found in our software overview with filter function. The exact design depends on the industry, company size and customising depth of the specific ERP setup. A well-founded answer always requires a look at the individual business processes and the strategic IT roadmap.
Which standards and regulations are relevant for OLAP?
Across all industries, GoBD and GDPR apply. Specific standards are documented in the main article. The exact design depends on the industry, company size and customising depth of the specific ERP setup.
What certifications or training are available for OLAP?
Training and certification offerings can be found under ERP training providers. Vendors often offer their own consultant certifications. The exact design depends on the industry, company size and customising depth of the specific ERP setup.
What alternatives to OLAP are there?
Alternatives and complementary concepts are examined in the main article — see also related glossary terms for distinctions. The exact design depends on the industry, company size and customising depth of the specific ERP setup. A well-founded answer always requires a look at the individual business processes and the strategic IT roadmap.
What is the difference from OLTP?
OLTP (Online Transaction Processing) is optimised for individual transactions (order creation, posting). OLAP is optimised for multidimensional analysis (aggregations, drill-down, comparisons). Practical relevance and implementation effort depend heavily on the existing system landscape and the business processes to be mapped.
Do I still need OLAP cubes today?
For very large data volumes (billions of records), yes. For medium volumes (up to ~50 million records), modern in-memory databases without a separate cube layer are often sufficient. Practical relevance and implementation effort depend heavily on the existing system landscape and the business processes to be mapped.
