Overview
This chapter covers the types of OLAP operations, the OLAP system, and its applications. OLAP is a key tool that provides valuable insights for data analysis. In this OLAP tutorial, we will introduce the fundamentals of OLAP and how it can optimize data processing.
Large corporations commonly use relational database systems. These systems are ideal for transaction-based environments, but they often present challenges when dealing with large amounts of data. Relational databases, although powerful, can be too complex for non-IT professionals to interpret vast volumes of data efficiently.
For example, a sales company might struggle to generate a report for a specific month when the data is too large. The system can slow down, and this performance lag can affect business operations. Fortunately, OLAP offers a solution to this problem by providing a more efficient way to process and analyze large datasets.
With OLAP, you can take a snapshot of the data at a particular point in time. The system allows for slicing, dicing, pivoting, and drill-down/drill-up analysis through various data hierarchies. This OLAP tutorial will help you understand these operations in a clear and practical manner.
Terminology
To understand OLAP better, it’s important to know some basic terminology. This tutorial will explain common OLAP terms:
Cube – A cube refers to a multi-dimensional dataset that contains both dimensions and measures. When there are more than three dimensions, it may be referred to as a hypercube.
Dimension – Dimensions consist of multiple members, each having attributes. Some dimensions are organized into hierarchies. These are defined at either the schema level or as part of a cube within a “Dimensions” element.
Measures – Measures are the quantities we analyze, typically numeric values. We perform various mathematical operations on measures.
Hierarchy – A hierarchy organizes data in a tree structure, with each member having one parent and zero or more child members. Children are the members in the next lower level in the hierarchy.
Level -Data within a hierarchy can be organized into different levels, such as Year, Quarter, Month, and Day, providing varying levels of detail.
OLAP Analysis
OLAP (Online Analytical Processing) systems allow for advanced data analysis. These systems help in analyzing multi-dimensional data, and they are widely used in data mining. OLAP makes it easier to process and store data in a multi-dimensional schema, providing users with powerful tools for exploration.
OLAP Operations
1. Slice
- Slicing refers to selecting a piece of the cube. It reduces the dimensionality of the cube by performing a selection on one dimension.
Example: In cooking, slicing a vegetable means cutting it into smaller pieces.
2.Dice
- Dicing selects two or more dimensions from a cube, resulting in a new sub-cube. This operation reduces the number of member values from one or more dimensions.
Drilling down or roll up is a specific analytical technique whereby the user navigates among hierarchy levels of data ranging from the most summarized (up)to the most detailed (down).
3.Drill Up(Roll up)
- Drilling up reduces data granularity by aggregating data. It involves climbing up a concept hierarchy for a dimension and summarizing data.
4.Drill Down(Roll down)
- Drilling down increases data granularity. This operation involves stepping down a concept hierarchy for a dimension and introducing additional dimensions.
5.Pivot
- Pivoting rotates the data axes to view the data from different perspectives. It helps present information in various ways to provide more useful insights.
All these terms are used to mean the presentation of information in a variety of different and useful ways.
Other OLAP Operations
- Drill Across
This operation involves executing a query across more than one fact table. - Drill Through
Drilling through allows you to explore data from the bottom level of a cube down to its backend relational tables.
Types of OLAP Systems
- Relational OLAP (ROLAP)
- Multidimensional OLAP (MOLAP)
- Hybrid OLAP (HOLAP)
To learn more about OLAP system types, click here.
OLAP Application
OLAP finds wide application in data management. Some common applications include:
- Financial Applications
- Marketing/Sales Applications
- Business Modeling Management
- Reporting
- Business Performance Management
To explore the power of OLAP with Saiku click here.
