Transcript What is Data Warehouse

CPIT 440
Data Mining and Warehouse
Lab3
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CPIT 440
Data Mining and Warehouse
Lab3: Outlines
• Introduction to Data Warehouse
– What is Data Warehouse ?
– Difference between Data Warehouse and Database
• Introduction to OLAP operations
– Introduction to cubes
– Cube structure
– OLAP Operations
• Exercises
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CPIT 440
Data Mining and Warehouse
Data Warehouse
• What is Data Warehouse ?
– A data warehouse is a repository of an organization's
stored data that is designed for query and analysis
rather than for transaction processing to facilitate
reporting and analysis.
– It usually contains historical data derived from
transaction data, but it can include data from other
sources.
– It separates analysis workload from transaction
workload and enables an organization to consolidate
data from several sources.
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CPIT 440
Data Mining and Warehouse
Data Warehouse
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CPIT 440
Data Mining and Warehouse
Difference between Data
Warehouse and Database
• A question we often asks out in the field is:
I already have a database, so why do I need a
data warehouse ? What is the difference
between a database vs. a data warehouse?
Database
Data Warehouse
Designed to handle
transactions
It is structured to make
analytics fast and easy.
It isn’t designed to handle and
do analytics well.
It exists as a layer on top of
another database or
databases, and takes the data
from all these databases and
creates a layer optimized for
and dedicated to analytics.
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CPIT 440
Data Mining and Warehouse
Introduction to OLAP Operations
• Introduction to cubes:
– A cube is a set of data that is usually constructed from
a subset of a data warehouse and is organized and
summarized into a multidimensional structure defined
by a set of dimensions and measures.
– Cubes are the main objects in online analytic
processing (OLAP),
– It is a technology that provides fast access to data in a
data warehouse.
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CPIT 440
Data Mining and Warehouse
Introduction to OLAP Operations
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CPIT 440
Data Mining and Warehouse
Introduction to OLAP Operations
• Cube Structure:
– Every cube has a schema, which is the set of joined
tables in the data warehouse from which the cube
draws its source data.
– The central table in the schema is the fact table, the
source of the cube's measures.
– The other tables are dimension tables, the sources of
the cube's dimensions.
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CPIT 440
Data Mining and Warehouse
Introduction to OLAP Operations
• Cube Structure
– A cube's structure is defined by its measures and
dimensions.
– They are derived from tables in the cube's data source.
– The set of tables from which a cube's measures and
dimensions are derived is called the cube's schema.
– Every cube schema consists of a single fact table and
one or more dimension tables.
– The cube's measures are derived from columns in the
fact table.
– The cube's dimensions are derived from columns in the
dimension tables.
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CPIT 440
Data Mining and Warehouse
Introduction to OLAP Operations
• Cube Structure
– Star schema: A fact table in the middle connected to a
set of dimension tables
– Snowflake schema: A refinement of star schema
where some dimensional hierarchy is normalized into a
set of smaller dimension tables, forming a shape
similar to snowflake
– Fact constellations: Multiple fact tables share
dimension tables, viewed as a collection of stars,
therefore called galaxy schema or fact constellation
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CPIT 440
Data Mining and Warehouse
Introduction to OLAP Operations
• OLAP Operations:
– Roll up: summarize data / dimension reduction
– Roll down: reverse of roll-up
• Make detailed data, or introducing new dimensions
– Slice and dice
– Pivot (rotate)
•
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CPIT 440
Data Mining and Warehouse
Roll up and Roll down
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CPIT 440
Data Mining and Warehouse
Slice and Dice
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CPIT 440
Data Mining and Warehouse
Pivot (Rotate)
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CPIT 440
Data Mining and Warehouse
Exercise 1
• Suppose that a data warehouse consists of the
three dimensions: time, doctor, and patient, and
the two measures count and charge, where
charge is the fee that a doctor charges a patient
for a visit.
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CPIT 440
Data Mining and Warehouse
Exercise 1
(a) Enumerate three classes of schemas that are
popularly used for modeling data warehouses.
Three classes of schemas popularly used for modeling data
warehouses are
• The star schema,
• The snowflake schema
• The fact constellations schema.
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CPIT 440
Data Mining and Warehouse
Exercise 1
(b) Draw a schema diagram for the above data
warehouse using one of the schema classes
listed in part (a).
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CPIT 440
Data Mining and Warehouse
Exercise 1
(c) Starting with the base cuboid [day; doctor;
patient], what specific OLAP operations should
be performed in order to list the total fee
collected by each doctor in 2004?
The operations to be performed are:
• Roll-up on time from day to year.
• Slice for time=2004.
• Roll-up on patient from individual patient to all.
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CPIT 440
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Exercise 2
• Suppose that a data warehouse for Big
University consists of the following four
dimensions: student, course, semester, and
instructor, and two measures count and avg.
grade.
• When at the lowest conceptual level (e.g.,for a
given student, course, semester, and instructor
combination), the avg. grade measure stores the
actual course grade of the student.
• At higher conceptual levels, avg. grade stores
the average grade for the given combination.
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CPIT 440
Data Mining and Warehouse
Exercise 2
(a) Draw a snowflake schema diagram for the data
warehouse.
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CPIT 440
Data Mining and Warehouse
Exercise 2
(b) Starting with the base cuboid [student; course;
semester; instructor], what specific OLAP
operations should perform in order to list the
average grade of CS courses for each Big
University student.
The specific OLAP operations to be performed are:
• Roll-up on course from course id to department.
• Roll-up on student from student id to university.
• Dice on course, student with department=\CS" and
university = \Big University".
• Drill-down on student from university to student
name.
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CPIT 440
Data Mining and Warehouse
Exercise 3
• Suppose that a data warehouse consists of the
four dimensions; date, spectator, location, and
game, and the two measures, count and charge,
where charge is the fee that a spectator pays
when watching a game on a given date.
• Spectators may be students, adults, or seniors,
with each category having its own charge rate.
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CPIT 440
Data Mining and Warehouse
Exercise 3
(a) Draw a star schema diagram for the data
warehouse.
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CPIT 440
Data Mining and Warehouse
Exercise 3
(b) Starting with the base cuboid [date; spectator;
location; game], what specific OLAP operations
should perform in order to list the total charge
paid by student spectators at GM Place in 2004?
The specific OLAP operations to be performed are:
• Roll-up on date from date id to year.
• Roll-up on spectator from spectator id to status.
• Roll-up on location from location id to location name.
• Roll-up on game from game id to all.
• Dice with status=\students", location name=\GM
Place", and year=2004.
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