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1. SAP HANA Foundation

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Problem: Heterogeneous Information
Sources
“Heterogeneities are everywhere”
 Different interfaces
 Different data representations
 Duplicate and inconsistent information
Personal
Databases
Digital Libraries
Scientific Databases
World
Wide
Web

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Problem: Data Management in Large
Enterprises
• Vertical fragmentation of informational systems
(vertical stove pipes)
• Result of application (user)-driven development
of operational systems
Sales Administration Finance Manufacturing ...
Sales Planning
Stock Mngmt
...
Suppliers
...
Debt Mngmt
Num. Control
...
Inventory

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Goal: Unified Access to Data
Integration System
 Collects and combines information
 Provides integrated view, uniform user interface
 Supports sharing
World
Wide
Web
Digital Libraries Scientific Databases
Personal
Databases

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 Two Approaches:
 Query-Driven (Lazy)
 Warehouse (Eager)
Source Source
?
Why a Warehouse?

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The Traditional Research Approach
Source SourceSource
. . .
Integration System
. . .
Metadata
Clients
Wrapper WrapperWrapper
 Query-driven (lazy, on-demand)

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Disadvantages of Query-Driven
Approach
 Delay in query processing
 Slow or unavailable information sources
 Complex filtering and integration
 Inefficient and potentially expensive for
frequent queries
 Competes with local processing at sources

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The Warehousing Approach
Data
Warehouse
Clients
Source SourceSource
. . .
Extractor/
Monitor
Integration System
. . .
Metadata
Extractor/
Monitor
Extractor/
Monitor
 Information
integrated in
advance
 Stored in wh for
direct querying
and analysis

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Advantages of Warehousing Approach
• High query performance
– But not necessarily most current information
• Doesn’t interfere with local processing at sources
– Complex queries at warehouse
– OLTP at information sources
• Information copied at warehouse
– Can modify, annotate, summarize, restructure, etc.
– Can store historical information
– Security, no auditing

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Not Either-Or Decision
• Query-driven approach still better for
– Rapidly changing information
– Rapidly changing information sources
– Truly vast amounts of data from large numbers of
sources
– Clients with unpredictable needs

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What is a Data Warehouse?
A Practitioners Viewpoint
“A data warehouse is simply a single,
complete, and consistent store of data
obtained from a variety of sources and made
available to end users in a way they can
understand and use it in a business context.”
-- Barry Devlin, IBM Consultant

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What is a Data Warehouse?
An Alternative Viewpoint
“A DW is a
– subject-oriented,
– integrated,
– time-varying,
– non-volatile
collection of data that is used primarily in
organizational decision making.”
-- W.H. Inmon, Building the Data Warehouse, 1992

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A Data Warehouse is...
• Stored collection of diverse data
– A solution to data integration problem
– Single repository of information
• Subject-oriented
– Organized by subject, not by application
– Used for analysis, data mining, etc.
• Optimized differently from transaction-
oriented db
• User interface aimed at executive

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… Cont’d
• Large volume of data (Gb, Tb)
• Non-volatile
– Historical
– Time attributes are important
• Updates infrequent
• May be append-only
• Examples
– All transactions ever at Sainsbury’s
– Complete client histories at insurance firm
– LSE financial information and portfolios

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Generic Warehouse Architecture
Extractor/
Monitor
Extractor/
Monitor
Extractor/
Monitor
Integrator
Warehouse
Client Client
Design Phase
Maintenance
Loading
...
Metadata
Optimization
Query & Analysis

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Data Warehouse Architectures:
Conceptual View
• Single-layer
– Every data element is stored once only
– Virtual warehouse
• Two-layer
– Real-time + derived data
– Most commonly used approach in
industry today
“Real-time data”
Operational
systems
Informational
systems
Derived Data
Real-time data
Operational
systems
Informational
systems

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Three-layer Architecture: Conceptual
View
• Transformation of real-time data to derived
data really requires two steps
Derived Data
Real-time data
Operational
systems
Informational
systems
Reconciled Data
Physical Implementation
of the Data Warehouse
View level
“Particular informational
needs”

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Data Warehousing: Two Distinct Issues
(1) How to get information into warehouse
“Data warehousing”
(2) What to do with data once it’s in warehouse
“Warehouse DBMS”
• Both rich research areas
• Industry has focused on (2)

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Issues in Data Warehousing
• Warehouse Design
• Extraction
– Wrappers, monitors (change detectors)
• Integration
– Cleansing & merging
• Warehousing specification & Maintenance
• Optimizations
• Miscellaneous (e.g., evolution)

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 OLTP: On Line Transaction Processing
 Describes processing at operational sites
 OLAP: On Line Analytical Processing
 Describes processing at warehouse
OLTP vs. OLAP

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Warehouse is a Specialized DB
Standard DB (OLTP)
• Mostly updates
• Many small transactions
• Mb - Gb of data
• Current snapshot
• Index/hash on p.k.
• Raw data
• Thousands of users (e.g.,
clerical users)
Warehouse (OLAP)
 Mostly reads
 Queries are long and complex
 Gb - Tb of data
 History
 Lots of scans
 Summarized, reconciled data
 Hundreds of users (e.g.,
decision-makers, analysts)

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