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DATA
WAREHOUSE
By: RAVI RANJAN
By: Ravi Ranjan
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DEFINITIONData Warehouse A collection of corporate information, derived directly from operational systems and some external data sources. Its specific purpose is to support business decisions, not business operations.
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THE PURPOSE OF DATA WAREHOUSING
Realize the value of data Data / information is an asset Methods to realize the value, (Reporting,
Analysis, etc.)
Make better decisions Turn data into information Create competitive advantage Methods to support the decision making
process, (EIS, DSS, etc.)
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Data Warehouse Components
• Staging Area• A preparatory repository where
transaction data can be transformed for use in the data warehouse
• Data Mart • Traditional dimensionally modeled set of
dimension and fact tables• Per Kimball, a data warehouse is the union
of a set of data marts • Operational Data Store (ODS)
• Modeled to support near real-time reporting needs.
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DATA WAREHOUSE FUNCTIONALITY
Data Warehouse Engine
Optimized LoaderExtractionCleansing
AnalyzeQuery
Metadata Repository
RelationalDatabases
LegacyData
Purchased Data
ERPSystems
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EVOLUTION ARCHITECTURE OF DATA WAREHOUSE
Top-Down Architecture
Bottom-Up Architecture
Enterprise Data Mart Architecture
Data Stage/Data Mart Architecture
GO TO DIAGRAM
GO TO DIAGRAM
GO TO DIAGRAM
GO TO DIAGRAM
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VERY LARGE DATA BASES
Terabytes -- 10^12 bytes:
Petabytes -- 10^15 bytes:
Exabytes -- 10^18 bytes:
Zettabytes -- 10^21 bytes:
Zottabytes -- 10^24 bytes:
Wal-Mart -- 24 Terabytes
Geographic Information Systems
National Medical Records
Weather images
Intelligence Agency Videos
WAREHOUSES ARE VERY LARGE DATABASES
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COMPLEXITIES OF CREATING A DATA WAREHOUSE
Incomplete errors Missing FieldsRecords or Fields That, by Design, are
not Being Recorded
Incorrect errorsWrong Calculations, AggregationsDuplicate RecordsWrong Information Entered into Source
System
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SUCCESS & FUTURE OF DATA WAREHOUSE
The Data Warehouse has successfully supported the
increased needs of the State over the past eight
years.
The need for growth continues however, as the
desire for more integrated data increases.
The Data Warehouse has software and tools in place
to provide the functionality needed to support new
enterprise Data Warehouse projects.
The future capabilities of the Data Warehouse can be
expanded to include other programs and agencies.
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DATA WAREHOUSE PITFALLS
You are going to spend much time extracting, cleaning, and loading data
You are going to find problems with systems feeding the data warehouse
You will find the need to store/validate data not being captured/validated by any existing system
Large scale data warehousing can become an exercise in data homogenizing
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DATA WAREHOUSE PITFALLS…
The time it takes to load the warehouse will expand to the amount of the time in the available window... and then some
You are building a HIGH maintenance system You will fail if you concentrate on resource
optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer
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BEST PRACTICES
Complete requirements and design
Prototyping is key to business understanding
Utilizing proper aggregations and detailed
data
Training is an on-going process
Build data integrity checks into your system.
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BACK TO ARCHITECTURE
Top-Down Architecture
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BACK TO ARCHITECTURE
Bottom-Up Architecture
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Enterprise Data Mart Architecture
BACK TO ARCHITECTURE
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Data Stage/Data Mart Architecture
BACK TO ARCHITECTURE
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Thank You