welcome
Presented By,Neenu C. Paul(12120051)CS B, S7SOE, CUSAT
Guided By,Dr. Sudheep ElayidomDivision of Computer ScienceSOE, CUSAT
CONTENTS• What is a data warehouse?
• What is data warehousing?
• Database vs Data warehouse
• OLTP & OLAP
• Data warehouse architecture
• Multidimensional data model
• Data Mart
• ETL
• Advantages of data warehouse
• Disadvantages of data warehouse
• S/W Solutions of data warehouse
• Conclusion
• References
A producer wants to know….
Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
What is the most
effective distribution
channel?
What product prom-
-otions have the biggest
impact on revenue? What impact will
new products/services
have on revenue
and margins?
Which customers
are most likely to go
to the competition ?
What is a Data Warehouse??
• A data warehouse is an appliance for storing and analyzing data, and reporting.
• Central database that includes information from several different sources.
• Keeps current as well as historical data.
• Used to produce reports to assist in decision-making and management.
“Data Warehouse is a subject
oriented, integrated, time-
variant and non-volatile
collection of data in support of
management’s decision making
process.” – W. H. Inmon
Data Warehouse
Subject Oriented
Integrated
Time Variant
Non-volatile
What is Data Warehousing?
A process of transforming data into information and making it available to users in a timely enough manner to make a difference
Data
Information
Database vs Data Warehouse
Database
• Transaction Oriented
• For saving online bargain data
• E-R modeling techniques are used for designing
• Capture data
• Constitute real time information
Data Warehouse
• Subject oriented
• For saving historical data
• Data modeling techniques are used for designing.
• Analyze data
• Constitute entire information base for all time.
Data Processing Technologies
• OLTP (on-line transaction processing)
- The major task is to perform on-line transaction and query processing. Covers most of the day-to-day operations of an organization.
• OLAP(On-Line Analytical Processing)
- Serve knowledge workers(users) in the role of data analysis and decision making.
- Organize and present data in various formats to accommodate the diverse needs of the different users.
Data Processing
Technologies
OLTP OLAP
OLTP vs OLAP OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write dozens of records Millions of record read
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
11 October 31, 2014
To summarize ...
OLTP Systems are
used to “run” a business
The Data Warehouse helps
to “optimize” the business
Typical DW Architecture
System B
System C
System D
System A
Extract
Transform
Load
The Data
Warehouse
Bu
sin
ess M
od
el
Self Serve
Data Sources ETL Data Store Data Access Presentation
Prompted Views
Dashboards
Scorecards
Ad-Hoc Reporting
12
Multidimensional data model
• Developed for implementing data warehouse and data marts.
• Provides both a mechanism to store data and a way for business analysis.
• An alternative to entity-relationship (E/R) model
TYPES OF MULTIDIMENSIONAL DATA MODEL
Data cube model.
Star schema model.
Snow flake schema model.
Fact Constellations.
Data cubes
• A data warehouse is based on a multidimensional data model which views data in the form of a data cube.
• Three important concepts are associated with data cubes
- Slicing
- Dicing
- Rotating
•In the cube given below we have the results of the 1991 Canadian Census with ethnic origin, age group and geography representing the dimensions of the cube, while 174 represents the measure. The dimension is a category of data. Each dimension includes different levels of categories. The measures are actual data values that occupy the cells as defined by the dimensions selected.
1991 Canadian Census
15
Slicing the Data Cube
• Figure 2 illustrates slicing the Ethnic origin Chinese. When the cube is sliced like in this example, we are able to generate data for Chinese origin for the geography and age groups as a result.
• The data that is contained within the cube has effectively been filtered in order to display the measures associated only with the Chinese ethnic origin.
• From an end user perspective, the term slice most often refers to a two- dimensional page selected from the cube.
16
Dicing and Rotating
• Dicing is a related operation to slicing in which a sub-cube of the original space is defined
• Dicing provides the user with the smallest available slice of data, enabling you to examine each sub-cube in greater detail.
• Rotating, which is sometimes called pivoting changes the dimensional orientation of the report or page display from the cube data. Rotating may consist of swapping the rows an columns, or moving one of the row dimensions into the column dimension.
17
Data Mart
• Contains a subset of the data stored in the data warehouse that is of interest to a specific business community, department, or set of users.
• E.g.: Marketing promotions, finance ,or account collections.
• Data marts are small slices of the data warehouse.
• Data marts improve end-user response time by allowing users to have access to the specific type of data they need to view.
• A data mart is basically a condensed and more focused version of a data warehouse.
Data warehouse vs Data mart
DATA WAREHOUSE
• Holds multiple subject areas
• Holds very detailed information
• Works to integrate all data sources
• Does not necessarily use a dimensional model but feeds dimensional models
DATA MART
• Often holds only one subject area-for example, Finance, or Sales
• May hold more summarized data (although many hold full detail)
• Concentrates on integrating information from a given subject area or set of source systems
• Is built focused on a dimensional model using a star schema
Reasons for creating a data mart
• 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.
Advantages & Disadvantages of data warehousingAdvantages
Enhances end-user access to a wide variety of data.
Increases data consistency.
Increases productivity and decreases computing costs.
Is able to combine data from different sources, in one place.
It provides an infrastructure that could support changes to data and replication of the changed databack into the operational systems.
Disadvantages
Extracting, cleaning and loading data could be time consuming.
Problems with compatibility with systems already in place e.g. transaction processing system.
Providing training to end-users, who end up not using the data warehouse.
Security could develop into a serious issue, especially if the data warehouse is web accessible.
Applications of data warehousing
Industry Application
Finance Credit card Analysis
Insurance Claims, Fraud Analysis
Telecommunication Call record Analysis
Transport Logistics management
Consumer goods Promotion Analysis
etl
• Extract-Transform-Load
• Responsible for the operations taking place in the backstage of data warehouse architecture.
• Extract : Get the data from source system as efficiently as possible
• Transform : Perform calculations on data
• Load : Load the data in the target storage
ADVANTAGES OF ETL TOOL
Simple, faster and cheaper
Deliver good performance even for very large data set
Allows reuse of existing complex programs
Popular etl tools
Tools Company
Infomix IBM
Oracle Warehouse Builder ORACLE
Microsoft SQL Server Integration Microsoft
IBM Infomix
• Informix is one of the world’s most widely used database servers
• High levels of performance and availability, distinctive capabilities in data replication and scalability, and minimal administrative overhead.
HIGHLIGHTS
Real-time Analytics: Informix is a single platform that can power OLTP and OLAP workloads and successfully meet service-level agreements (SLAs) for each
Fast, Always-on Transactions: Provides one of the industry’s widest sets of options for keeping data available at all times, including zero downtime for maintenance
Sensor data management: Solves the big data challenge of sensor data with unmatched performance and scalability for managing time series data
Easy to Use: Informix runs virtually unattended with self-configuring, self-managing and self-healing capabilities
Best-of-breed embeddability: Provides a proven embedded data management platform for ISVs and OEMs to deliver integrated, world-class solutions, enabling platform independence
NoSQL capability:IBM Informix unleashes new capabilities, giving you a way to combine unstructured and structured data in a smart way, bringing NoSQL to your SQL database.
Data Warehousing is not a new phenomenon. All large
organizations already have data warehouses, but they are just not
managing them. Over the next few years, the growth of data
warehousing is going to be enormous with new products and
technologies coming out frequently. In order to get the most out of this
period, it is going to be important that data warehouse planners and
developers have a clear idea of what they are looking for and then
choose strategies and methods that will provide them with
performance today and flexibility for tomorrow.
conclusion
Reference
1) Data Mining , Gupta
2) Data Warehousing , C.S.R. Prabhu
3) Jeff Lawyer and Shamsul Chowdhury “Best Practices in Data Warehousing to Support Business Initiatiatives and Needs”, IEEE 2004
4) Ruilian Hou “Research and Analysis of Data Warehouse Technologies”, IEEE 2011
5) S. Sai Sathyanarayana Reddy, Dr. L.S.S.Reddy, Dr.V.Khanna, A.Lavanya “Advanced Techniques for Scientific Data Warehousing”, IEEE 2009
6) Murat Obali, Abdul Kadir Gorur, “A Real Time Data Warehouse Approach for Data Processing”, IEEE 2013
7) Ruilian Hou “Analysis and research on the difference between data warehouse and database”, IEEE 2011
Questions ????
THANK YOU!!!!!
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