OLAP in Business Intelligence

Rajan Kumar Upadhyayrajan24oct@gmail.com

OLAPOnline Analytic Processing tool is to store & manage data’s that can be effectively used to generate information. From the Business Intelligence Architecture view, OLAP sits between the Data Warehouse and End-user tools.

An OLAP (Online analytical processing) cube is a data structure that allows fast analysis of data.

OLAP CUBE

An OLAP cubes are aggregated set data’s that helps into rapid analysis.

OLAP cube can be understand that multidimensional array of datasets. For example a company might wish to analyze some financial data by product, by time-period, by city, by type of revenue and cost, and by comparing actual data with a budget.

The OLAP cube consists of numeric facts called measures which are categorized by dimensions. The cube metadata (structure) may be created from a star schema or snowflake schema of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

Star Schema

The star schema (sometimes referenced as star join schema) is the simplest style of data warehouse schema. The star schema consists of a few fact tables (possibly only one, justifying the name) referencing any number of dimension tables. The star schema is considered an important special case of the snowflake schema.

Star schema used by example query.

SnowFlake SchemaA snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake in shape. Closely related to the star schema, the snowflake schema is represented by centralized fact tables which are connected to multiple dimensions. In the snowflake schema, however, dimensions are normalized into multiple related tables whereas the star schema's dimensions are denormalized with each dimension being represented by a single table. When the dimensions of a snowflake schema are elaborate, having multiple levels of relationships, and where child tables have multiple parent tables ("forks in the road"), a complex snowflake shape starts to emerge. The "snowflaking" effect only affects the dimension tables and not the fact tables. Star schema used by example query.

OLAP & DATA WAREHOUSE

OLAP

• OLAP makes Business Intelligence happen, broadly by enabling the following:a) Transforming the data into multi-dimensional cubesb) Summarized pre-aggregated and derived datac) Strong query managementd) Multitude of calculation and modeling functions

DATA WAREHOUSE

• A data-warehouse could be having data in various formats like dimensional (with a high degree of de-normalization) OR highly relational (like 3rd normal form).

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OLAP & DATA WAREHOUSE

OLAP

• OLAP provides the building blocks to enable analysis Mostly the end-user tools (like business modeling tools, Data mining tools, performance reporting tools..), which sit on top of the OLAP to provide rich user Business Intelligence interface.

DATA WAREHOUSE

• Data warehouse work in conjunction to provide overall data-access for the end-user tools. Data warehouse has two approach 1)Query Driven ( Lazy)2) Warehouse (Eager)

BI Architecture

Types Of OLAP

OLAP

ROLAP MOLAP HOLAP Others

WOLAP DOLAP MOLAP SOLAP

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ROLAP - Relational OLAPROLAP works with data that resides in a relational database where the base data and dimension tables are stored as relational tables. This model permits multidimensional analysis of data as this enables users to perform a function equivalent to that of the traditional OLAP slicing and dicing feature. This is achieved thorough use of any SQL reporting tool to extract or ‘query’ data directly from the data warehouse. Wherein specifying a ‘Where clause’ equals performing a certain slice and dice action.

MOLAP - Multidimensional OLAPMultidimensional OLAP, with a popular acronym of MOLAP, is widely regarded as the classic form of OLAP. One of the major distinctions of MOLAP against a ROLAP tool is that data are pre-summarized and are stored in an optimized format in a multidimensional cube, instead of in a relational database. In this type of model, data are structured into proprietary formats in accordance with a client’s reporting requirements with the calculations pre-generated on the cubes. This is probably by far, the best OLAP tool to use in making analysis reports since this enables users to easily reorganize or rotate the cube structure to view different aspects of data. This is done by way of slicing and dicing. MOLAP analytic tool are also capable of performing complex calculations. Since calculations are predefined upon cube creation, this results in the faster return of computed data. MOLAP systems also provide users the ability to quickly write back data into a data set. Moreover, in comparison to ROLAP, MOLAP is considerably less heavy on hardware due to compression techniques. In a nutshell, MOLAP is more optimized for fast query performance and retrieval of summarized information. There are certain limitations to implementation of a MOLAP system, one primary weakness of which is that MOLAP tool is less scalable than a ROLAP tool as the former is capable of handling only a limited amount of data. The MOLAP approach also introduces data redundancy. There are also certain MOLAP products that encounter difficulty in updating models with dimensions with very high cardinality.

HOLAP - Hybrid OLAP

HOLAP is the product of the attempt to incorporate the best features of MOLAP and ROLAP into a single architecture. This tool tried to bridge the technology gap of both products by enabling access or use to both multidimensional database (MDDB) and Relational Database Management System (RDBMS) data stores. HOLAP systems stores larger quantities of detailed data in the relational tables while the aggregations are stored in the pre-calculated cubes. HOLAP also has the capacity to “drill through” from the cube down to the relational tables for delineated data.

Some of the advantages of this system are better scalability, quick data processing and flexibility in accessing of data sources.

Others : Web OLAP

Simply put, a Web OLAP which is likewise referred to as Web-enabled OLAP, pertains to OLAP application which is accessible via the web browser. Unlike traditional client/server OLAP applications, WOLAP is considered to have a three-tiered architecture which consists of three components: a client, a middleware and a database server.

Probably some of the most appealing features of this style of OLAP are the considerably lower investment involved, enhanced accessibility as a user only needs an internet connection and a web browser to connect to the data and ease in installation, configuration and deployment process.

But despite all of its unique features, it could still not compare to a conventional client/server machine. Currently, it is inferior in comparison to OLAP applications which involve deployment in client machines in terms of functionality, visual appeal and performance.

Others: DOLAP, MOLAP & SOLAP

Desktop OLAP(DOLAP)Desktop OLAP is based on the idea that a user can download a section of the data from the database or source, and work with that dataset locally, or on their desktop. DOLAP is easier to deploy and has a cheaper cost but comes with a very limited functionality in comparison with other OLAP applications.

Mobile OLAP (MOLAP)Mobile OLAP is merely refers to OLAP functionalities on a wireless or mobile device. This enables users to access and work on OLAP data and applications remotely thorough the use of their mobile devices.

Spatial OLAP (MOLAP)With the aim of integrating the capabilities of both Geographic Information Systems (GIS) and OLAP into a single user interface, “SOLAP” or Spatial OLAP emerged. SOLAP is created to facilitate management of both spatial and non-spatial data, as data could come not only in an alphanumeric form, but also in images and vectors. This technology provides easy and quick exploration of data that resides on a spatial database.