Data Warehousing - OLAP

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Introduction

Online Analytical Processing Server (OLAP) is based on multidimensional data model. It allows the managers , analysts to get insight the information through fast, consistent, interactive access to information. In this chapter we will discuss about types of OLAP, operations on OLAP, Difference between OLAP and Statistical Databases and OLTP.

Types of OLAP Servers

We have four types of OLAP servers that are listed below.

  • Relational OLAP(ROLAP)

  • Multidimensional OLAP (MOLAP)

  • Hybrid OLAP (HOLAP)

  • Specialized SQL Servers


Relational OLAP(ROLAP)

The Relational OLAP servers are placed between relational back-end server and client front-end tools. To store and manage warehouse data the Relational OLAP use relational or extended-relational DBMS.

ROLAP includes the following.

  • implementation of aggregation navigation logic.

  • optimization for each DBMS back end.

  • additional tools and services.

Multidimensional OLAP (MOLAP)

Multidimensional OLAP (MOLAP) uses the array-based multidimensional storage engines for multidimensional views of data.With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore many MOLAP Server uses the two level of data storage representation to handle dense and sparse data sets.

Hybrid OLAP (HOLAP)

The hybrid OLAP technique combination of ROLAP and MOLAP both. It has both the higher scalability of ROLAP and faster computation of MOLAP. HOLAP server allows to store the large data volumes of detail data. the aggregations are stored separated in MOLAP store.

Specialized SQL Servers

specialized SQL servers provides advanced query language and query processing support for SQL queries over star and snowflake schemas in a read-only environment.

OLAP Operations

As we know that the OLAP server is based on the multidimensional view of data hence we will discuss the OLAP operations in multidimensional data.

Here is the list of OLAP operations.

  • Roll-up

  • Drill-down

  • Slice and dice

  • Pivot (rotate)


Roll-up

This operation performs aggregation on a data cube in any of the following way:

  • By climbing up a concept hierarchy for a dimension

  • By dimension reduction.

Consider the following diagram showing the roll-up operation.

Roll-up
  • The roll-up operation is performed by climbing up a concept hierarchy for the dimension location.

  • Initially the concept hierarchy was "street < city < province < country".

  • On rolling up the data is aggregated by ascending the location hierarchy from the level of city to level of country.

  • The data is grouped into cities rather than countries.

  • When roll-up operation is performed then one or more dimensions from the data cube are removed.

Drill-down

Drill-down operation is reverse of the roll-up. This operation is performed by either of the following way:

  • By stepping down a concept hierarchy for a dimension.

  • By introducing new dimension.

Consider the following diagram showing the drill-down operation:


Drill-Down
  • The drill-down operation is performed by stepping down a concept hierarchy for the dimension time.

  • Initially the concept hierarchy was "day < month < quarter < year."

  • On drill-up the time dimension is descended from the level quarter to the level of month.

  • When drill-down operation is performed then one or more dimensions from the data cube are added.

  • It navigates the data from less detailed data to highly detailed data.


Slice

The slice operation performs selection of one dimension on a given cube and give us a new sub cube. Consider the following diagram showing the slice operation.

Slice
  • The Slice operation is performed for the dimension time using the criterion time ="Q1".

  • It will form a new sub cube by selecting one or more dimensions.

Dice

The Dice operation performs selection of two or more dimension on a given cube and give us a new subcube. Consider the following diagram showing the dice operation:

Dice

The dice operation on the cube based on the following selection criteria that involve three dimensions.

  • (location = "Toronto" or "Vancouver")

  • (time = "Q1" or "Q2")

  • (item =" Mobile" or "Modem").

Pivot

The pivot operation is also known as rotation.It rotates the data axes in view in order to provide an alternative presentation of data.Consider the following diagram showing the pivot operation.

Pivot

In this the item and location axes in 2-D slice are rotated.

OLAP vs OLTP

SNData Warehouse (OLAP)Operational Database(OLTP)
1This involves historical processing of information.This involves day to day processing.
2OLAP systems are used by knowledge workers such as executive, manager and analyst.OLTP system are used by clerk, DBA, or database professionals.
3This is used to analysis the business.This is used to run the business.
4It focuses on Information out.It focuses on Data in.
5This is based on Star Schema, Snowflake Schema and Fact Constellation Schema.This is based on Entity Relationship Model.
6It focuses on Information out.This is application oriented.
7This contains historical data.This contains current data.
8This provides summarized and consolidated data.This provide primitive and highly detailed data.
9This provide summarized and multidimensional view of data.This provides detailed and flat relational view of data.
10The number or users are in Hundreds.The number of users are in thousands.
11The number of records accessed are in millions.The number of records accessed are in tens.
12The database size is from 100GB to TBThe database size is from 100 MB to GB.
13This are highly flexible.This provide high performance.


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