Transcript Slide 1

Chapter 4
Data Warehousing
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Outline
• Definition of Data Warehouse
• Reasons for creating Data Marts
• Benefits and characteristics of Data Warehouse
• Reasons for need of data warehousing
• Operational and Informational Systems
• Data Warehouse vs Data Mart
• Types of Systems Used
• Data warehouse architectures
• List four steps of data reconciliation
• Design a data mart
• Star Schema
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Definition of Data Warehouse
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It is a huge central database that accepts, stores and
maintain data from different sources and locations.
Disparate sources may
use different formats and
technologies.
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Definition of Data Mart
• A data mart is a simple form of a data warehouse
that is focused on a single subject (or functional
area), such as sales, finance or marketing.
• Data marts are small slices of the data
warehouse.
• Data marts are often built and controlled by a
single department within an organization.
• Given their single-subject focus, data marts
usually draw data from only a few sources.
• The sources could be internal operational
systems, a central data warehouse, or external
data.
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Reasons for creating a data mart
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Easy access to frequently needed data
Creates collective view by a group of users
Improves end-user response time
Ease of creation
Lower cost than implementing a full data
warehouse
• Potential users are more clearly defined than in
a full data warehouse
• Contains only business essential data and is
less cluttered.
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Benefits of Data Warehouse
• Collect data from multiple sources into a single database so a
single query engine can be used to present data.
• Maintain data history, even if the source transaction systems
do not.
• Integrate data from multiple source systems, enabling a
central view across the enterprise.
• Improve data quality by flagging or even fixing bad data.
• Present the organization's information consistently (constantly
and reliably).
• Provide a single common data model for all data of interest
regardless of the data's source.
• Restructure the data so that it makes sense to the business
users.
• Making decision–support queries are easier to write.
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Example of using a Data Warehouse
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Characteristics of Data Warehouse
• A data warehouse is a system used for reporting and
data analysis.
• Integrating data from one or more disparate sources
creates a central repository of data, a data
warehouse (DW).
• Data warehouses store current and historical data
and are used for creating trending reports for senior
management reporting such as annual and quarterly
comparisons.
• The data stored in the warehouse is uploaded from
the operational systems.
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Operational and Informational Systems
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Data Warehouse Versus Data Mart
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Types of systems used (1)
Online Analytical Processing (OLAP)
• It is characterized by a low volume of transactions.
• Queries are often very complex and involve
aggregations.
• OLAP databases store aggregated, historical data in
multi-dimensional schemas (usually star schemas).
Online Transaction Processing (OLTP)
• Characterized by a large number of transactions
(INSERT, UPDATE, DELETE).
• OLTP databases contain detailed and current data.
• The schema used to store transactional databases is
the entity model (usually 3NF).
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Types of systems used (2)
Predictive analysis
• It is about finding and quantifying hidden
patterns in the data using complex
mathematical models that can be used to
predict future outcomes.
• Predictive analysis is different from OLAP in
that OLAP focuses on historical data analysis
and is reactive in nature, while predictive
analysis focuses on the future.
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Data Warehouse Architectures
• Generic Two-Level Architecture
• Independent Data Mart
• Dependent Data Mart and Operational Data
Store
• Logical Data Mart and Real-Time Data
Warehouse
• Three-Layer architecture
All involve some form of
extraction, transformation and loading (ETL)
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Generic two-level data warehousing architecture
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One,
companywide
warehouse
E
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Periodic extraction  data is not completely current in warehouse
Independent Data Mart
Data Warehousing Architecture
Data marts: Mini-warehouses, limited in scope
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Separate ETL for each
independent data mart
Data access complexity due
to multiple data marts
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Dependent data mart with operational data store:
a three-level architecture
ODS provides option for
obtaining current data
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Single ETL for
Enterprise Data Warehouse (EDW)
Simpler data access
Dependent data marts
loaded from EDW
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Logical data mart and real time warehouse architecture
ODS and data warehouse
are one and the same
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Near real-time ETL for
Data Warehouse
Data marts are NOT separate databases,
but logical views of the data warehouse
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 Easier to create new data marts
Three-layer data architecture for a data warehouse
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Data Characteristics: Status vs. Event Data
Status
Event = a database action
(create/update/delete) that
results from a transaction
Status
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Data Characteristics: Transient vs. Periodic Data
Transient
operational
data
With transient
data, changes
to
existing
records
are
written over
previous
records, thus
destroying
the previous
data content
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Data Characteristics: Transient vs. Periodic Data
Periodic
warehouse
data
Periodic data
are
never
physically
altered
or
deleted once
they
have
been added
to the store
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The Reconciled Data Layer
• Typical operational data is:
– Transient–not historical
– Not normalized (perhaps due to denormalization for
performance)
– Restricted in scope–not comprehensive
– Sometimes poor quality–inconsistencies and errors
• After ETL, data should be:
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–
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–
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Detailed–not summarized yet
Historical–periodic
Normalized–3rd normal form or higher
Comprehensive–enterprise-wide perspective
Timely–data should be current enough to assist decisionmaking
– Quality controlled–accurate with full integrity
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The ETL Process
• Capture/Extract
• Scrub or data cleansing
• Transform
• Load and Index
ETL = Extract, transform, and load
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Steps in data reconciliation (1)
Capture/Extract…obtaining a snapshot of a chosen subset of the source data
for loading into the data warehouse
Static extract = capturing a
snapshot of the source data at a
point in time
Incremental extract = capturing
changes that have occurred since
the last static extract
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Steps in data reconciliation (2)
Scrub/Cleanse…uses pattern recognition and AI techniques to upgrade data
quality
Fixing errors: misspellings,
Also: decoding, reformatting, time
erroneous dates, incorrect field usage, stamping, conversion, key generation,
mismatched addresses, missing data, merging,
error
detection/logging,
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duplicate data, inconsistencies
locating missing data
Steps in data reconciliation (3)
Transform = convert data from format of operational system to format of data
warehouse
Record-level:
Field-level:
Selection–data partitioning
Joining–data combining
Aggregation–data summarization
single-field–from one field to one field
multi-field–from many fields to one, or
one field to many
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Steps in data reconciliation (4)
Load/Index= place transformed data into the warehouse and create indexes
Refresh mode: bulk rewriting of
target data at periodic intervals
Update mode: only changes in source
data are written to data warehouse
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Single-field transformation
In general–some transformation
function translates data from old
form to new form
Algorithmic transformation uses
a formula or logical expression
Table lookup–another approach,
uses a separate table keyed by
source record code
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Multifield transformation
M:1–from many source fields to one
target field
1:M–from one source field
to many target fields
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Star Schema
• The star schema separates business
process data into facts.
• Facts hold the measurable, quantitative
data about a business, and dimensions
which are descriptive attributes related
to fact data.
• Examples of fact data include sales
price, sale quantity, and time, distance,
speed, and weight measurements.
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Components of a star schema
Fact tables contain factual or
quantitative data
1:N relationship between dimension
tables and fact tables
Dimension tables are denormalized to
maximize performance
Dimension tables contain descriptions about
the subjects of the business
Excellent for ad-hoc queries, but bad for online transaction processing
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Star schema example
Fact table provides statistics for
sales broken down by product,
period and store dimensions
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Star schema with sample data
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