Online analytical processing (OLAP) allows users to easily extract and analyze data from different perspectives. It originated in the 1970s and was formalized in 1993, with OLAP cubes organizing numeric facts by dimensions to enable fast analysis. OLAP provides operations like roll-up, drill-down, slice, and dice to analyze aggregated data across multiple systems. It offers advantages over relational databases for consistent reporting and analysis.
2. OVERVIEW
• INTRODUCTION
• HISTORY OF OLAP
• OLAP CUBE
• DIFFERENCE BETWEEN OLAP & OLTP
• OLAP OPERATIONS
• ADVANTAGES & DISADVANTAGES
3. INTRODUCTION TO OLAP
• OLAP (online analytical processing) is computer
processing that enables a user to easily and
selectively extract and view data from different
points of view.
• OLAP allows users to analyze database information
from multiple database systems at one time.
4. HISTORY
• In 1993, E. F. Codd came up with the term
online analytical processing (OLAP) and proposed 12
criteria to define an OLAP database
• The term OLAP seems perfect to describe databases
designed to facilitate decision making (analysis) in an
organization
• The first product that performed OLAP queries was
Express, which was released in 1970 (and acquired
by Oracle in 1995 from Information Resources).
5. Some popular OLAP server software programs
include:
Oracle Express Server
Hyperion Solutions Essbase
OLAP processing is often used for data mining.
OLAP products are typically designed for multiple-
user environments, with the cost of the
software based on the number of users.
7. OLAP CUBE
• An OLAP Cube is a data structure that allows
fast analysis of data.
• The arrangement of data into cubes
overcomes a limitation of relational databases.
• The OLAP cube consists of numeric facts called
measures which are categorized by
dimensions.
9. OLTP VS OLAP
• Source of data
• Purpose of data
• Queries
• Processing speed
• Space Requirement
• Database Design
• Backup and Recovery
10. OPERATIONS OF OLAP
• There are different kind of operations which we
can perform in OLAP
• Roll up
• Drill Down
• Slice
• Dice
• Pivot
• Drill-across
• Drill-through
11. ROLL UP
• Takes the current aggregation level of fact values
and does a further aggregation on one or more of
the dimensions.
• Equivalent to doing GROUP BY to this dimension by
using attribute hierarchy.
SELECT [attribute list], SUM [attribute names]
FROM [table list]
WHERE [condition list]
GROUP BY [grouping list];
12. DRILL DOWN
• Summarizes data at a lower level of a dimension
hierarchy.
• Increases a number of dimensions - adds new headers
13. SLICE
Performs a selection on one dimension of the given
cube. Sets one or more dimensions to specific values
and keeps a subset of dimensions for selected values.
14. DICE
Define a sub-cube by performing a selection of one or
more dimensions. Refers to range select condition on
one dimension, or to select condition on more than one
dimension. Reduces the number of member values of
one or more dimensions.
15. PIVOT
• Rotates the data axis to view the data from
different perspectives.
• Groups data with different dimensions.
16. DRILL-ACROSS AND DRILL-
THROUGH
Drill-across : Accesses more than one fact table that is
linked by common dimensions. Combines cubes that
share one or more dimensions.
•Drill-through: Drill down to the bottom level of a data
cube down to its back-end relational tables.
18. ADVANTAGES
• Consistency of Information and calculations
• What if" scenarios
• It allows a manager to pull down data from an
OLAP database in broad or specific terms.
• OLAP creates a single platform for all the
information and business needs, planning,
budgeting, forecasting, reporting and analysis
20. PURPOSE OF OLAP
• To derive summarized information from large volume
database
• To generate automated reports for human view
21. CONCLUSION
• OLAP is a significant improvement over query systems
• OLAP is an interactive system to show different
summaries of multidimensional data by interactively
selecting the attributes in a multidimensional data
cube