OLAP stands for Online Analytical Processing.
It uses database tables (fact and dimension tables) to enable
multidimensional viewing, analysis and querying of large amounts of data.
E.g. OLAP technology could provide management with fast answers to
complex queries on their operational data or enable them to analyze their
company’s historical data for trends and patterns.
Online Analytical Processing (OLAP) applications and tools are those
that are designed to ask ―complex queries of large multidimensional collections
of data.‖ Due to that OLAP is accompanied with data warehousing.
The key driver of OLAP is the multidimensional nature of the business
These problems are characterized by retrieving a very large number of
records that can reach gigabytes and terabytes and summarizing this data into a
form information that can by used by business analysts.
One of the limitations that SQL has, it cannot represent these complex
A query will be translated in to several SQL statements. These SQL
statements will involve multiple joins, intermediate tables, sorting,
aggregations and a huge temporary memory to store these tables.
These procedures required a lot of computation which will require a long
time in computing.
The second limitation of SQL is its inability to use mathematical models
in these SQL statements. If an analyst, could create these complex statements
using SQL statements, still there will be a large number of computation and
huge memory needed.
Therefore the use of OLAP is preferable to solve this kind of problem.