Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.

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Data Warehousing
Relational OLAP (ROLAP)
Ch Anwar ul Hassan (Lecturer)
Department of Computer Science and Software
Engineering
Capital University of Sciences & Technology, Islamabad
Pakistan
anwarchaudary@gmail.com

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Relational OLAP (ROLAP)

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Why ROLAP?
Issue of scalability i.e. curse of dimensionality
for MOLAP
 Deployment of significantly large dimension tables
as compared to MOLAP using secondary storage.
 Aggregate awareness allows using pre-built
summary tables by some front-end tools.
 Star schema designs usually used to facilitate
ROLAP querying (in next lecture).

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ROLAP as a “Cube”
 OLAP data is stored in a relational database (e.g. a
star schema)
 The fact table is a way of visualizing as a “un-rolled”
cube.
 So where is the cube?
 It’s a matter of perception
 Visualize the fact table as an elementary cube.
Product
Time
500Z1P2M2
250Z1P1M1
Sale K Rs.ZoneProductMonth
Fact Table

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How to create “Cube” in ROLAP
 Cube is a logical entity containing values of a
certain fact at a certain aggregation level at an
intersection of a combination of dimensions.
 The following table can be created using 3 queries
SUM
(Sales_Amt)
M1 M2 M3 ALL
P1
P2
P3
Total
Month_ID
Product_ID
Part 2Part 1
Part 3

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 For the table entries, without the totals
SELECT S.Month_Id, S.Product_Id,
SUM(S.Sales_Amt)
FROM Sales
GROUP BY S.Month_Id, S.Product_Id;
 For the row totals
SELECT S.Product_Id, SUM (Sales_Amt)
FROM Sales
GROUP BY S.Product_Id;
 For the column totals
SELECT S.Month_Id, SUM (Sales)
FROM Sales
GROUP BY S.Month_Id;
How to create “Cube” in ROLAP using SQL

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CUBE Clause
 The CUBE clause is part of SQL:1999
 GROUP BY CUBE (v1, v2, …, vn)
 Equivalent to a collection of GROUP BYs, one for
each of the subsets of v1, v2, …, vn

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ROLAP & Space Requirement
If one is not careful, with the increase in number of
dimensions, the number of summary tables gets very
large.

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ROLAP Issues
 Maintenance.
 Non standard hierarchy of dimensions.
 Non standard conventions.
Aggregation pit-falls.

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 Maximum performance boost implies using
lots of disk space for storing every pre-
calculation.
 Minimum performance boost implies no disk
space with zero pre-calculation.
 Using meta data to determine best level of
pre-aggregation from which all other
aggregates can be computed.
Performance vs. Space Trade-Off

11. Performance vs. Space Trade-off using Wizard
20
40
60
80
100
2 4 6 8


MB
%Gain Aggregation answers
most queries
Aggregation
answers few queries
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HOLAP
 Target is to get the best of both worlds.
 HOLAP (Hybrid OLAP) allow co-existence of
pre-built MOLAP cubes alongside relational
OLAP or ROLAP structures.

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DOLAP
Cube on the
remote server
Local
Machine/Server
Subset of the cube is
transferred to the local
machine

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End