Introduction

OLAP or Online Analytical Processing is software that permits information analytics of several databases at any instant of time. It provides many viewpoints by viewing the extracted business data and is useful when data analysts have to join, aggregate and group data. The OLAP operations in an RDBM system is resource-intensive and delivers speedy analysis since the Online Analytical Processing data can be pre-aggregated and pre-calculated. 

In this article let us look at:

  1. OLAP cube
  2. Basic analytical operations of OLAP
  3. Types of OLAP systems
  4. Advantages of OLAP
  5. Disadvantages of OLAP

1. OLAP cube

OLAP- Online Analytical Processing databases are cubical structures such that viewing and creating reports of the data is as simple as dividing data into OLAP cubes. The cube also known as hypercube has measures or numeric facts in its dimensions. Normally, analysis and operations on data are done in a spreadsheet having a column and row format for data values or 2D data. Online Analytical Processing databases allow multi-dimensional data from multivariate sources to be represented on its cubical structure and allows for orderly and logical operations.

How does Online Analytical Processing work? The data from a data warehouse has multiple formats, data sources and several representations like excel sheet, text files, multimedia files, and so on. Such data is first extracted; then cleaned and transformed using Online Analytical Processing operations on the Online Analytical Processing cube server where it is stored in its pre-aggregated and pre-calculated form for analysis.

2. Basic analytical operations of OLAP

The 4 Online Analytical Processing analytical operations are briefly discussed below.

  • Roll-up
  • Drill-down
  • Slice and dice
  • Pivot (rotate)
  • Roll-up:

Roll-up is the operation used for aggregation and consolidation of data achieved in 2 ways.

1) Ascending the concept hierarchy which is an orderly grouping system of items in a level. 

2) Dimensional Reduction.

Take as an example the following cube diagram

Here the cities of Lost Angles, New Jersey etc are rolled up into the USA country group. The sales figure for these 2 cities are added and rolled up. Thus in the consolidation process, the data from the location hierarchy is rolled up from the city to the group of the country. The roll-up process removes 1 or more dimensions like the Quarter dimension removed here.

  • Drill-down fragments into smaller parts and is the roll-up process opposite achieved through dimensional addition or moving down the concept hierarchy.
  • Slice creates a new sub-cube from the selected dimension.The Time dimension is sliced with the filter of Q1 to create the new Online Analytical Processing example of the quarter cube.
  • Dice operation is like the slice operation but with 2 or more dimensions selected to form the sub-cube.
  • Pivot operation rotates the axes of the data to give a substituted data cube example presentation as in the pivot example of item types shown below. 

3. Types of OLAP systems

The Online Analytical Processing types structural-hierarchy is diagrammatically represented below.

The types of cubes are

  • ROLAP- Relational Online Analytical Processing which is the extended RDBMS multidimensional data mapped into the relational operations performance.
  • MOLAP- Multidimensional Online Analytical Processing uses multidimensional data for its operations.
  • HOLAP- Hybrid Online Analytical Processing where the aggregated data totals are stored in a multidimensional RDB and the details stored in another RDB offering MOLAP performance and ROLAP efficiency.
  • DOLAP-Desktop Online Analytical Processing is a cheap limited functionality option of Online Analytical Processing characteristics that allows a data part to be downloaded locally for analysis. 
  • WOLAP-Web Online Analytical Processing for 3-tier architecture on web browsers has a database server, middleware and client interface.
  • Mobile Online Analytical Processing works with mobile devices.
  • SOLAP- Spatial Online Analytical Processing uses GIS to manage both non-spatial and spatial data.
      

4. Advantages of OLAP

Online Analytical Processing features platform can be used for business Online Analytical Processing applications like reporting, planning, analysis, budgeting etc in an orderly, logical Online Analytical Processing cube-consistent form of calculations and information. It can easily search specifically and generally for the Online Analytical Processing database and performs a quick analysis of the search for “What if” scenarios. Online Analytical Processing is the tool for a business model’s having both performance reporting and data mining feature tools and allow dice, slice operations on the data cube using filters, measures and other dimensions. Its advantages of Online Analytical Processing work well with time-series analysis, finding the outliers, clusters etc making it a fast-response visualization tool of the analytics and processing system.

5. Disadvantages of OLAP

The concept of Online Analytical Processing does have certain disadvantages like the complicated snowflake or star schema required for Online Analytical Processing tools in data organization. One cannot have large numbers of multi-dimensions represented on one Online Analytical Processing cube and there are issues in accessing transactional data. Besides, it is time-consuming to do full-updates of the Online Analytical Processing architecture when any modification is done on the Online Analytical Processing cube. 

Conclusion

We have just studied the Online Analytical Processing technology, the concept of the Online Analytical Processing Cube and how it is used for performing the standard 5 operations on an Online Analytical Processing vs OLTP basis on business data allowing analysts to view and extract multi-dimensional data and present different viewpoints of such data. Online Analytical Processing systems also have advantages and disadvantages of Online Analytical Processing with various types catering to a gamut of modern-day servers.

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