Data%20Warehousing

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Data Warehousing

• University of California, Berkeley
• School of Information Management and Systems
• SIMS 257 Database Management

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Lecture Outline

• Review
• Extending OR database systems
• Java and JDBC
• Data Warehouses
• Introduction to Data Warehouses
• Data Warehousing
• (Based on lecture notes from Joachim Hammer,
  University of Florida, and Joe Hellerstein and
  Mike Stonebraker of UCB)

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Lecture Outline

• Review
• Extending OR database systems
• Java and JDBC
• Data Warehouses
• Introduction to Data Warehouses
• Data Warehousing
• (Based on lecture notes from Joachim Hammer,
  University of Florida, and Joe Hellerstein and
  Mike Stonebraker of UCB)

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PostgreSQL Extensibility

• Postgres is extensible because its operation is
  catalog-driven
• RDBMS store information about databases, tables,
  columns, etc., in what are commonly known as
  system catalogs. (Some systems call this the data
  dictionary).
• One key difference between Postgres and standard
  RDBMS is that Postgres stores much more
  information in its catalogs
• not only information about tables and columns,
  but also information about its types, functions,
  access methods, etc.
• These classes can be modified by the user, and
  since Postgres bases its internal operation on
  these classes, this means that Postgres can be
  extended by users
• By comparison, conventional database systems can
  only be extended by changing hardcoded procedures
  within the DBMS or by loading modules
  specially-written by the DBMS vendor.

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A more complex function

• To illustrate a simple SQL function, consider the
  following, which might be used to debit a bank
  account
• create function TP1 (int4, float8) returns int4
• as update BANK set balance BANK.balance -
  2
• where BANK.acctountno 1
• select balance from bank
• where accountno 1 language
  'sql'
• A user could execute this function to debit
  account 17 by 100.00 as follows
• select (x TP1( 17,100.0))

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SQL Functions on Composite Types

• When creating functions with composite types, you
  have to include the attributes of that argument.
  If EMP is a table containing employee data,
  (therefore also the name of the composite type
  for each row of the table) a function to double
  salary might be
• CREATE FUNCTION double_salary(EMP) RETURNS
  integer
• AS ' SELECT 1.salary 2 AS salary '
  LANGUAGE SQL
• SELECT name, double_salary(EMP) AS dream FROM EMP
  WHERE EMP.cubicle point '(2,1)'
• name dream
• -------------
• Sam 2400
• Notice the use of the syntax 1.salary to select
  one field of the argument row value. Also notice
  how the calling SELECT command uses a table name
  to denote the entire current row of that table as
  a composite value.

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New Type Definition

• In the external language (usually C) functions
  are written for
• Type input
• From a text representation to the internal
  representation
• Type output
• From the internal represenation to a text
  representation
• Can also define function and operators to
  manipulate the new type

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Rules System

• CREATE RULE name AS ON event
• TO object WHERE condition
• DO INSTEAD action NOTHING
• Rules can be triggered by any event (select,
  update, delete, etc.)

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Java and JDBC

• Java is probably the high-level language used in
  most software development today one of the
  earliest enterprise additions to Java was JDBC
• JDBC is an API that provides a mid-level access
  to DBMS from Java applications
• Intended to be an open cross-platform standard
  for database access in Java
• Similar in intent to Microsofts ODBC

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JDBC

• Provides a standard set of interfaces for any
  DBMS with a JDBC driver using SQL to specify
  the databases operations.

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JDBC Simple Java Implementation
import java.sql. import oracle.jdbc. public
class JDBCSample public static void
main(java.lang.String args) try //
this is where the driver is loaded
//Class.forName("jdbc.oracle.thin")
DriverManager.registerDriver(new
OracleDriver()) catch (SQLException e)
System.out.println("Unable to load driver
Class") return
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JDBC Simple Java Impl.
try //All DB access is within the
try/catch block... // make a connection to
ORACLE on Dream Connection con
DriverManager.getConnection(
"jdbcoraclethin_at_dream.sims.berkel
ey.edu1521dev", mylogin",
myoraclePW") // Do an SQL statement...
Statement stmt con.createStatement()
ResultSet rs stmt.executeQuery("SELECT NAME
FROM DIVECUST")
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JDBC Simple Java Impl.
// show the Results... while(rs.next())
System.out.println(rs.getString("NAME"))
// Release the database
resources... rs.close()
stmt.close() con.close() catch
(SQLException se) // inform user of
errors... System.out.println("SQL Exception
" se.getMessage()) se.printStackTrace(Syst
em.out)
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Lecture Outline

• Review
• Extending OR database systems
• Java and JDBC
• Data Warehouses
• Introduction to Data Warehouses
• Data Warehousing
• (Based on lecture notes from Joachim Hammer,
  University of Florida, and Joe Hellerstein and
  Mike Stonebraker of UCB)

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Overview

• Data Warehouses and Merging Information Resources
• What is a Data Warehouse?
• History of Data Warehousing
• Types of Data and Their Uses
• Data Warehouse Architectures
• Data Warehousing Problems and Issues

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Problem Heterogeneous Information Sources
Heterogeneities are everywhere
Personal Databases
World Wide Web
Scientific Databases
Digital Libraries

• Different interfaces
• Different data representations
• Duplicate and inconsistent information

Slide credit J. Hammer
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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 Planning
Suppliers
Num. Control
Stock Mngmt
Debt Mngmt
Inventory
...
...
...
Sales Administration
Finance
Manufacturing
...
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Goal Unified Access to Data
Personal Databases
Digital Libraries
Scientific Databases

• Collects and combines information
• Provides integrated view, uniform user interface
• Supports sharing

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The Traditional Research Approach

• Query-driven (lazy, on-demand)

Clients
Metadata
Integration System
. . .
Wrapper
Wrapper
Wrapper
. . .
Source
Source
Source
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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
• Hasnt caught on in industry

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The Warehousing Approach

• 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
• Doesnt 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
• Has caught on in industry

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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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Data Warehouse Evolution
Building the DW Inmon (1992)
Data Replication Tools
Relational Databases
Company DWs
2000
1995
1990
1985
1980
1960
1975
Information- Based Management
Data Revolution
Middle Ages
Prehistoric Times
TIME
PCs and Spreadsheets
End-user Interfaces
1st DW Article
DW Confs.
Vendor DW Frameworks
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What is a Data Warehouse?

• A Data Warehouse is a
• subject-oriented,
• integrated,
• time-variant,
• non-volatile
• collection of data used in support of management
  decision making processes.
• -- Inmon Hackathorn, 1994 viz. Hoffer, Chap 11

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DW Definition

• Subject-Oriented
• The data warehouse is organized around the key
  subjects (or high-level entities) of the
  enterprise. Major subjects include
• Customers
• Patients
• Students
• Products
• Etc.

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DW Definition

• Integrated
• The data housed in the data warehouse are defined
  using consistent
• Naming conventions
• Formats
• Encoding Structures
• Related Characteristics

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DW Definition

• Time-variant
• The data in the warehouse contain a time
  dimension so that they may be used as a
  historical record of the business

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DW Definition

• Non-volatile
• Data in the data warehouse are loaded and
  refreshed from operational systems, but cannot be
  updated by end-users

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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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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 decision makers
  and analysts

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Contd

• Large volume of data (Gb, Tb)
• Non-volatile
• Historical
• Time attributes are important
• Updates infrequent
• May be append-only
• Examples
• All transactions ever at WalMart
• Complete client histories at insurance firm
• Stockbroker financial information and portfolios

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Warehouse is a Specialized DB

• Standard DB
• 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
• 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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Summary
Business Information Guide
Business Information Interface
Data Warehouse
Data Warehouse Catalog
Data Warehouse Population
Operational Systems
Enterprise Modeling
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Warehousing and Industry

• Warehousing is big business
• 2 billion in 1995
• 3.5 billion in early 1997
• Predicted 8 billion in 1998 Metagroup
• WalMart has largest warehouse
• 900-CPU, 2,700 disk, 23 TB Teradata system
• 7TB in warehouse
• 40-50GB per day

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Types of Data

• Business Data - represents meaning
• Real-time data (ultimate source of all business
  data)
• Reconciled data
• Derived data
• Metadata - describes meaning
• Build-time metadata
• Control metadata
• Usage metadata
• Data as a product - intrinsic meaning
• Produced and stored for its own intrinsic value
• e.g., the contents of a text-book

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Data Warehousing Architecture
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Ingest
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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

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Three-layer Architecture Conceptual View

• Transformation of real-time data to derived data
  really requires two steps

View level Particular informational needs
Physical Implementation of the Data Warehouse
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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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Data Warehousing Two Distinct Issues

• (1) How to get information into warehouse
• Data warehousing
• (2) What to do with data once its in warehouse
• Warehouse DBMS
• Both rich research areas
• Industry has focused on (2)

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Data Extraction

• Source types
• Relational, flat file, WWW, etc.
• How to get data out?
• Replication tool
• Dump file
• Create report
• ODBC or third-party wrappers

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Wrapper

• Converts data and queries from one data model to
  another

Queries
Data Model A
Data

• Extends query capabilities for sources with
  limited capabilities

Queries
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Wrapper Generation

• Solution 1 Hard code for each source
• Solution 2 Automatic wrapper generation

Wrapper Generator
Definition
Wrapper
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Data Transformations

• Convert data to uniform format
• Byte ordering, string termination
• Internal layout
• Remove, add reorder attributes
• Add key
• Add data to get history
• Sort tuples

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Monitors

• Goal Detect changes of interest and propagate to
  integrator
• How?
• Triggers
• Replication server
• Log sniffer
• Compare query results
• Compare snapshots/dumps

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Data Integration

• Receive data (changes) from multiple
  wrappers/monitors and integrate into warehouse
• Rule-based
• Actions
• Resolve inconsistencies
• Eliminate duplicates
• Integrate into warehouse (may not be empty)
• Summarize data
• Fetch more data from sources (wh updates)
• etc.

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Data Cleansing

• Find ( remove) duplicate tuples
• e.g., Jane Doe vs. Jane Q. Doe
• Detect inconsistent, wrong data
• Attribute values that dont match
• Patch missing, unreadable data
• Notify sources of errors found

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Warehouse Maintenance

• Warehouse data ? materialized view
• Initial loading
• View maintenance
• View maintenance

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Differs from Conventional View Maintenance...

• Warehouses may be highly aggregated and
  summarized
• Warehouse views may be over history of base data
• Process large batch updates
• Schema may evolve

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Differs from Conventional View Maintenance...

• Base data doesnt participate in view maintenance
• Simply reports changes
• Loosely coupled
• Absence of locking, global transactions
• May not be queriable

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Warehouse Maintenance Anomalies

• Materialized view maintenance in loosely coupled,
  non-transactional environment
• Simple example

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Warehouse Maintenance Anomalies
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Maintenance Anomaly - Solutions

• Incremental update algorithms (ECA, Strobe, etc.)
• Research issues Self-maintainable views
• What views are self-maintainable
• Store auxiliary views so original auxiliary
  views are self-maintainable

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Self-Maintainability Examples

• Sold(item,clerk,age)
• Sale(item,clerk) Emp(clerk,age)
• Inserts into Emp
• If Emp.clerk is key and Sale.clerk is foreign key
  (with ref. int.) then no effect
• Inserts into Sale
• Maintain auxiliary view
• Emp-?clerk,age(Sold)
• Deletes from Emp
• Delete from Sold based on clerk

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Self-Maintainability Examples

• Deletes from Sale
• Delete from Sold based on item,clerk
• Unless age at time of sale is relevant
• Auxiliary views for self-maintainability
• Must themselves be self-maintainable
• One solution all source data
• But want minimal set

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Partial Self-Maintainability

• Avoid (but dont prohibit) going to sources
• SoldSale(item,clerk) Emp(clerk,age)
• Inserts into Sale
• Check if clerk already in Sold, go to source if
  not
• Or replicate all clerks over age 30
• Or ...

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Warehouse Specification (ideally)
View Definitions
Warehouse Configuration Module
Warehouse
Integration rules
Integrator
Metadata
Change Detection Requirements
Extractor/ Monitor
Extractor/ Monitor
Extractor/ Monitor
...
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Optimization

• Update filtering at extractor
• Similar to irrelevant updates in constraint and
  view maintenance
• Multiple view maintenance
• If warehouse contains several views
• Exploit shared sub-views

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Additional Research Issues

• Historical views of non-historical data
• Expiring outdated information
• Crash recovery
• Addition and removal of information sources
• Schema evolution

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More Information on DW

• Agosta, Lou, The Essential Guide to Data
  Warehousing. Prentise Hall PTR, 1999.
• Devlin, Barry, Data Warehouse, from Architecture
  to Implementation. Addison-Wesley, 1997.
• Inmon, W.H., Building the Data Warehouse. John
  Wiley, 1992.
• Widom, J., Research Problems in Data
  Warehousing. Proc. of the 4th Intl. CIKM Conf.,
  1995.
• Chaudhuri, S., Dayal, U., An Overview of Data
  Warehousing and OLAP Technology. ACM SIGMOD
  Record, March 1997.