This document discusses the evolution of data warehousing and its relationship to big data. It provides definitions of data warehousing, describing it as a subject-oriented collection of integrated and non-volatile data used for decision making. Key concepts like facts, dimensions, and data marts are introduced. The document then contrasts online transaction processing databases with data warehouses. Challenges of data warehousing are mentioned. The remainder covers big data characteristics, information architectures for structured and unstructured data, Oracle tools for big data, and how data analytics is shifting from reporting to prediction.

1. Information and Data - Relevance to Business
Prepared by Sharath Bhujani
Oracle India
IASA India

2. Agenda
IASA India
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Evolution: Long Story Short
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Data Warehouse: Terms & Concepts
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Architecture Overview
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OLTP Vs Data Warehouse
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Data Modeling
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Data Warehouse Challenges

3. Data Warehousing - Evolution
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Terms, dimensions, and facts were developed way back in 1960s.
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The concept of data warehousing dates back to the late 1980s.
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Using operational data for decision making became a necessity.
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Access to valuable information in the quickest possible time was key to success.
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There was a need for an architectural framework to move data from operational systems to a decision support environment.
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Forefathers: Bill Inmon and Ralph Kimball

4. Data Warehouse: Definition
“A data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions.”
— W.H. Inmon
“An enterprise-structured repository of subject-oriented, time-variant, historical data used for information retrieval and decision support. The data warehouse stores atomic and summary data.”
— Oracle’s definition of a data warehouse

5. Data Warehouse: Terms & Concepts
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Fact
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Dimension
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Data Mart
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Conformed Dimension

6. Architecture Overview
Metadata repository
Source systems
Staging area
Presentation
area
Access
tools

7. OLTP Database vs Data Warehouse
OLTP Database
Data Warehouse
Transactional data (current)
Data analysis (historical)
Stores detailed data
Stores summarized data
Data is dynamic (insert, update)
Data is largely static (no updates)
Transactions are repetitive
Ad hoc reporting
Application-oriented design
Subject-oriented design

8. Data Warehouse Challenges
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Changing business needs vs changing IT infrastructure
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Dealing with unstructured data

9. Agenda
IASA India
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Data Warehousing & Big Data
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Big Data Information Architecture
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Oracle Integrated Hardware & Software Solution
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Change / Evolution
Big Data

10. Data Warehousing and Big Data
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What is Big Data?
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Big Data characteristics: Volume, Velocity, Variety.
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Big data and data warehousing share the same basic goals.
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Type of data: big data Vs data warehouse
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Bringing Big Data into Enterprise Data Warehouse.

11. Enterprise Unstructured Data Growth

12. Traditional Information Architecture Approach
Big Data Information Architecture Approach

13. Data Modeling – Structured Vs Unstructured
Dimensional Modeling - Star Schema

14. Data Modeling – Structured Vs Unstructured
Key
Value
ID
172
Name
Sony LED TV WXYZ
Category 1
TV
Category 2
LED TV
Model
WXYZ
Make
Sony
A row from ‘Product’ table
ID
Name
Category 1
Category 2
Model
Make
172
Sony LED TV WXYZ
TV
LED TV
WXYZ
Sony
Dimensional Modeling - Star Schema

15. Oracle’s Integrated Hardware & Software Solution

16. Oracle Engineered Systems

17. Oracle Integrated Software Solution

18. Oracle Big Data Appliance
With the recent introduction of Oracle Big Data Appliance, Oracle became one of the first vendor to offer a complete and integrated engineered solution to address the full spectrum of enterprise big data requirements. Oracle’s Big Data strategy: Evolve your current enterprise data architecture to incorporate big data and deliver business value.

19. Change / Evolution

20. Analyzing Data - New Possibilities
Traditional Data Sources – Reporting
New Data Sources - Predicting

21. How Big Data Can Bring Chance: Insurance Domain
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Acquire:
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Driving habits, breaking pattern, average driving distance etc.
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Organize:
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Derive information on your driving habits, breaking pattern etc.
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Analyze:
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Analyze derived data with other information such as traffic conditions & your profile data. Perform risk analysis etc.
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Decide:
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Decide on the premium i.e. you can have a personalized insurance plan.

22. Thank you
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Evolution: Long story short
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Data Warehouse Architecture
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OLTP Vs Data Warehouse
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Data Warehouse Challenges
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Big Data Information Architecture
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Tools for Big Data
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Change / Evolution
Conclusion