Dw & etl concepts

Business Intelligence,
Data Warehousing &
ETL Concepts

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Business Intelligence
How intelligent can you make your business processes?
What insight can you gain into your business?
How integrated can your business processes be?
How much more interactive can your business be with customers, partners,
employees and managers?

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What is Business Intelligence (BI)?
Business Intelligence is a generalized term applied to a broad category of
applications and technologies for gathering, storing, analyzing and providing
access to data to help enterprise users make better business decisions
Business Intelligence applications include the activities of decision support
systems, query and reporting, online analytical processing (OLAP), statistical
analysis, forecasting, and data mining
An alternative way of describing BI is: the technology required to turn raw data
into information to support decision-making within corporations and business
processes

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Why BI?
BI technologies help bring decision-makers the data in a form they can quickly
digest and apply to their decision making.
BI turns data into information for managers and executives and in general, people
making decisions in a company.
Companies want to use technology tactically to make their operations more
effective and more efficient - Business intelligence can be the catalyst for that
efficiency and effectiveness.

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Benefits
The benefits of a well-planned BI implementation are going to be closely tied to
the business objectives driving the project.
Identify trends and anomalies in business operations more quickly, allowing
for more accurate and timelier decisions.
Deliver actionable insight and information to the right place with less effort .
Identify and operate based on a single version of the truth, allowing all
analysis to be completed on a core foundation with confidence.

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Business Intelligence Components
TRANSFORM
LOAD
EXTRACT
OLAPDATA
MINING
Data
Warehouse
Operational Data

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Business Intelligence Architecture

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Business Intelligence Technologies
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP
Data Warehouses / Data Marts
Data Exploration
OLAP, DSS, EIS, Querying and Reporting
Data Mining
Information discovery
Data Presentation
Visualization Techniques
Decision Making
Increasing potential to
support business decisions End User
Business Analyst
Data Analyst
DB Admin

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What is a Data Warehouse?
A data warehouse is a relational database that is designed for query and analysis
rather than for transaction processing. It usually contains historical data derived
from transaction data.
A data warehouse environment includes an extraction, transportation,
transformation, and loading (ETL) solution, online analytical processing (OLAP)
and data mining capabilities, client analysis tools, and other applications that
manage the process of gathering data and delivering it to business users.
It is a series of processes, procedures and tools (h/w & s/w) that help the
enterprise understand more about itself, its products, its customers and the
market it services

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Who are the potential
Customers ?
Which Products are sold the
most ?
What are the region-wise
preferences ?
What are the competitor
products ?
What are the projected
sales ?
What if you sale more
quantity of a particular
product ?
What will be the impact
on revenue ?
Results of promotion
schemes introduced ?
Why Data Warehousing?
Need of Intelligent Information in Competitive Market

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OLTP vs. Data Warehouse
OLTP systems are tuned for known transactions and workloads while workload is
not known in a data warehouse
Special data organization, access methods and implementation methods are
needed to support data warehouse queries (typically multidimensional queries)
e.g., average amount spent on phone calls between 9AM-5PM in Pune during
the month of December

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OLTP vs. Data Warehouse
OLTP
Application Oriented
Used to run business
Detailed data
Current up to date
Isolated Data
Repetitive access
Clerical User
WAREHOUSE (DSS)
Subject Oriented
Used to analyze business
Summarized and refined
Snapshot data
Integrated Data
Ad-hoc access
Knowledge User (Manager)

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OLTP vs Data Warehouse
OLTP
Performance Sensitive
Few Records accessed at a time (tens)
Read/Update Access
No data redundancy
Database Size 100MB -100 GB
DATA WAREHOUSE
Performance relaxed
Large volumes accessed at
a time(millions)
Mostly Read (Batch Update)
Redundancy present
Database Size 100 GB -
few terabytes

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OLTP vs Data Warehouse
OLTP
Transaction throughput is the
performance metric
Thousands of users
Managed in entirety
Data Warehouse
Query throughput is the
performance metric
Hundreds of users
Managed by subsets

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Data Warehouse Architectures
 Centralized
In a centralized architecture, there exists only one data warehouse which stores
all data necessary for business analysis. As already shown in the previous section,
the disadvantage is the loss of performance in opposite to distributed approaches.
Central Architecture

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Tiered:
A tiered architecture is a distributed data approach. This process
can not be done in one step because many sources have to be
integrated into the warehouse.
On a first level, the data of all branches in one region is collected, in
the second level the data from the regions is integrated into one
data warehouse.
Advantages:
 Faster response time
because the data is
located closer to the client
applications and
 Reduced volume of data to
be searched.
Tiered Architecture
Data Warehouse Architectures Contd…

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Metadata
Data Sources Data Management Access
Complete Warehouse Solution Architecture
Operational Data
Legacy Data
The Post
VISA
External Data
Sources
Enterprise
Data
Warehouse
Organizationally
structured
Extract
Transform
Load
Data Information Knowledge
Asset Assembly (and Management) Asset Exploitation
Data
Mart
Data
Mart
Departmentally
structured
Data
Mart
Sales
Inventory
Purchase

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Introduction To Data Marts
What is a Data Mart
From the Data Warehouse , atomic data flows to various departments for their
customized needs. If this data is periodically extracted from data warehouse
and loaded into a local database, it becomes a data mart. The data in Data Mart
has a different level of granularity than that of Data Warehouse. Since the data
in Data Marts is highly customized and lightly summarized , the departments can
do whatever they want without worrying about resource utilization. Also the
departments can use the analytical software they find convenient. The cost of
processing becomes very low.

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Data Mart Overview
Data Marts
Satisfy 80% of
the local end-
users’ requests
Sales Representatives
and Analysts
Human
Resources
Financial Analysts,
Strategic Planners,
and Executives
DM Marketing
DM Finance
DM Sales
DM HR
Data Warehouse
DM Sales
DM HR
DM Marketing

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From The Data Warehouse To Data Marts
Departmentally
Structured
Individually
Structured
Data Warehouse
Organizationally
Structured
Less
More
History
Normalized
Detailed
Data
Information

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 Data model is a conceptual representation of data structures
(tables) required for a database and is very powerful in
expressing and communicating the business requirements. A
data model is an abstract model that describes how data is
represented and used.
The term data model has two generally accepted meanings:
 A data model theory i.e. a formal description of how data may
be structured and used.
 A data model instance i.e. applying a data model theory to
create a practical data model instance for some particular
application.
Modeling Fundamentals:
What is Data Model ?

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Logical Data Model (LDM) - A logical design is conceptual and abstract.
The process of logical design involves arranging data into a series of logical
relationships called entities and attributes.
 Logical data model includes all required entities, attributes, key groups, and
relationships that represent business information and define business rules.
Modeling Fundamentals:Modeling Fundamentals:
Types OF Data ModelingTypes OF Data Modeling
Logical Data Model

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Physical Data Model (PDM) - A physical data model is a representation
of a data design which takes into account the facilities and constraints of a
given database management system.
 A complete physical data model will include all the database artifacts
required to create relationships between tables or achieve performance
goals, such as indexes, constraint definitions, linking tables, partitioned
tables or clusters.
Physical Data Model

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Entity relationship diagram (ERD) – A data model utilizing several
notations to depict data in terms of the entities and relationships described by
that data.
 Databases are used to store structured data. The structure of this data, together
with other constraints, can be designed using a variety of techniques, one of
which is called entity-relationship modeling or ERM.
ERD Diagram

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Important Terminologies –
 Entity – Are the principal data object about which information is to be collected.
A class of persons, places, objects, events, or concepts about which we need to
capture and store data.
•Persons: agency, contractor, customer,
department, division, employee, instructor,
student, supplier.
•Places: sales region, building, room,
branch office, campus.
•Objects: book, machine, part, product, raw material,
software license, software package, tool, vehicle model,
vehicle.
•Events: application, award, cancellation, class, flight,
invoice, order, registration, renewal, requisition,
reservation, sale, trip.
•Concepts: account, block of time, bond, course, fund,
qualification, stock.

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 Relationship – A natural business association that exists between
one or more entities. The relationship may represent an event that
links the entities or merely a logical affinity that exists between the
entities
 An example of a relationship would be:
• Employees are assigned to projects
• Student enrolling in a curriculum
• Projects have subtasks
• Departments manage one or more projects
STUDENT CURRICULUM
Is being studied by is enrolled in

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Dimensional Data Modeling (DDM) -Dimensional modeling is the
design concept used by many data warehouse designers to build their data
warehouse.
 Is a logical design technique that seeks to present the data in a standard, intuitive
framework that allows for high-performance access. It adheres to a discipline that
uses the relational model with some important restrictions.
 Every dimensional model is composed of one table with a multi-part key, called
the fact table, and a set of smaller tables called dimension tables.
Components of a DM:
 Fact Table
 Dimension table
 Attributes
 Good examples of dimensions are location, product, time, promotion,
organization etc. Dimension tables store records related to that particular
dimension and no facts (measures) are stored in these tables.
 A fact (measure) table contains measures (sales gross value, total units sold) and
dimension columns. These dimension columns are actually foreign keys from the
respective dimension tables.

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 End users cannot understand or navigate ER models
 Software cannot usefully query an ER model
 Use of ER modeling techniques defeats intuitive and high performance retrieval of
data
Why Dimensional Modeling?Why Dimensional Modeling?
When the designer places understandability and
performance as the highest goals . . .
Dimensional Modeling is the natural approach

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What is a Star Schema ?
Each dimension table has
a single-part primary key
that corresponds exactly to
one of the components of
the multi-part key in the
fact table. This
characteristic "star-like"
structure is often called a
star-schema.

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 The Star schema model is essentially a method to store data which are multi-
dimensional in nature, in a relational database. It consists of a single “fact table"
with a compound primary key, with one segment for each “dimension" and with
additional columns of additive, numeric facts.
What is a Star Schema ?
Customer
OrganizationTime
Product
Channel
SALES
The star schema makes multi-dimensional database (MDDB)
functionality possible using a traditional relational database.

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Fact Tables
 A fact table, because it has a multi-part primary key made up of
two or more foreign keys, always expresses a many-to-many
relationship.
 The most useful fact tables also contain one or more numerical
measures, or "facts," that occur for the combination of keys
that define each record.
 The most useful facts in a fact table are numeric. Numeric
addition is crucial because data warehouse applications rarely
retrieve a single fact table record. Rather, they retrieve
hundreds, thousands, or even millions of these records at a
time, and the only useful thing to do with so many records is to
add them up.

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Defining Fact Table Structure
ITEM_ID
WEEK_ID
STORE_ID
SALES_DOLLARS
SALES_UNITS
Fact Item Day Store
Item
Store
Week
Fact Columns
Fact Table Structure

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What is a Dimension?
Data Warehouse is
• Subject-Oriented
•Integrated
• Time-Variant
• Non-volatile
Subject Dimension
In a Dimensional Model, context of the measurements are represented in
dimension tables
The Dimension Attributes are the various columns in a dimension table

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What are Slow changing Dimensions?
Slowly changing dimensions are dimensions where a "constant"
actually evolves slowly and asynchronously.
“ Dimensions have been assumed to be independent of time”
In the real world this is not
strictly true
Examples: Humans change their name
Get married or divorced

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Three Methods…
The three choices for dealing with slow changing
dimensions are:
Approach Results
Type 1:
Type 2
Overwriting the old values in
the dimension record
Losing the ability to track the
old history
Creating an additional dimension
record at the time of the change
with the new attribute values
Segmenting history very
accurately between the old
description and the new
description
Type 3: Creating new “current” fields Describe history both

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Type one
Implementing Type 1:
 Overwrite the field with new value
 No effect anywhere else in the database
Scenarios where applicable:
 When original data was in error
 When no value is reviewed in keeping the old description/attribute
Advantages
Easy to implement
No key affected
Disadvantages
History is lost

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Type two
Advantages
Automatically partitions history
No time constraints required
Disadvantages
Abrupt point of time constraints
not effective
Implementing Type 2:
 Create new record with unique key
 Generalize the dimensioning by adding 2 or 3 various digits to the end of the
key.
Scenarios where applicable:
 Most commonly used where history is of importance

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Dimension Tables
 Dimension tables, most often contain descriptive textual information.
 Dimension attributes are used as the source of most of the interesting constraints
in data warehouse queries, and they are virtually always the source of the row
headers in the SQL answer set.
 It should be obvious that the power of the data warehouse is proportional to the
quality and depth of the dimension tables.

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Attributes in a Dimension Table
 Allows users to constrain data by one or more attributes.
 Allows users to define aggregation levels for data
DEPT CLASS SALES
Dept 1
Dept 2
Class 101
Class 120
Class 133
Class 127
Class 141
Class 145
1000
1100
1900
2100
1500
1800
• Present Classes by Departments
• Aggregate by Class
• Qualify by Department

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ETL !!!
(Extract, Transform, Load) –
ETL refers to the methods involved in accessing and manipulating source
data and loading it into target database. During the ETL process, more often,
data is extracted from an OLTP database, transformed to match the data
warehouse schema, and loaded into the data warehouse database.

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EXTRACT DATA FROM
DISPARATE SOURCES
TRANSFORM DATA
LOAD DATA WHERE
WE WANT TO
WHAT IS ETL?
E  EXTRACT
T  TRANSFORM
L  LOAD

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EXTRACTION (Data Capturing)
The ETL extraction element is responsible for extracting data from the source system.
During extraction, data may be removed from the source system or a copy made and the
original data retained in the source system.

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Legacy systems may require too much effort to implement such offload processes, so
legacy data is often copied into the data warehouse, leaving the original data in place.
Extracted data is loaded into the data warehouse staging area (a relational database
usually separate from the data warehouse database), for manipulation by the remaining
ETL processes.
EXTRACTION (Data Transmission)

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EXTRACTION (Cleansing Process)
Data extraction is generally performed within the source system itself.
Data extraction processes can be implemented using Transact-SQL stored procedures,
Data Transformation Services (DTS) tasks, or custom applications developed in
programming or scripting languages.

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TRANSFORMATION
The ETL transformation element is responsible for data validation, data accuracy, data
type conversion, and business rule application. An ETL system that uses inline
transformations during extraction is less robust and flexible than one that confines
transformations to the reformatting element. Transformations performed in the OLTP
system impose a performance burden on the OLTP database.

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TRANSFORMATION (contd.)
Data Validation
Check that all rows in the fact table match rows in dimension tables to enforce data
integrity.
Data Accuracy
Ensure that fields contain appropriate values, such as only "off" or "on" in a status field.
Data Type Conversion
Ensure that all values for a specified field are stored the same way in the data
warehouse regardless of how they were stored in the source system. For example, if
one source system stores "off" or "on" in its status field and another source system
stores "0" or "1" in its status field, then a data type conversion transformation converts
the content of one or both of the fields to a specified common value such as "off" or
"on".
Business Rule Application
Ensure that the rules of the business are enforced on the data stored in the
warehouse. For example, check that all customer records contain values for both
FirstName and LastName fields.

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LOADING
The ETL loading element is responsible for loading transformed data into the data
warehouse database.
Data warehouses are usually updated periodically rather than continuously, and large
numbers of records are often loaded to multiple tables in a single data load.
The data warehouse is often taken offline during update operations so that data can be
loaded faster and SQL Server 2000 Analysis Services can update OLAP cubes to
incorporate the new data. BULK INSERT, bcp, and the Bulk Copy API are the best tools
for data loading operations.
The design of the loading element should focus on efficiency and performance to
minimize the data warehouse offline time.

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ETL Tools
What are ETL Tools?
ETL Tools are meant to extract, transform and load the data into Data Warehouse for
decision making. Before the evolution of ETL Tools, the above mentioned ETL process
was done manually by using SQL code created by programmers. This task was tedious
and cumbersome in many cases since it involved many resources, complex coding and
more work hours. On top of it, maintaining the code placed a great challenge among the
programmers
Selecting an appropriate ETL tool is the most important decision that has to be made
when choosing the components of a data warehousing application. The ETL tool
operates at the heart of the data warehouse, extracting data from multiple data sources,
transforming the data to make it accessible to business analysis, and loading multiple
target databases

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Features of ETL Tools
Features of ETL Tools
The ETL tools have the ability to extract data from various sources like RDBMS ,
DB2 , COBOL data files and flat files at scheduled intervals , do required
transformation and load the data into Data Warehouse which resides on RDBMS.
The ETL tools can connect to a RDBMS and get the list of tables and their
attributes. The general steps for designing an ETL process are
Define the structure of source data
Define the structure of Destination Data
Map elements of source data to elements of destination data
Define the transformation required like changing values , summing
Schedule the execution of process
The process once executed , generates a log showing status of process ,
number of records inserted etc. Various reports about processes are available
which can form the Metadata.

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