2004.04.13- slide 1is 257 – spring 2004 data warehousing university of california, berkeley school...

IS 257 – Spring 2004 2004.04.13- SLIDE 1

Data Warehousing

University of California, Berkeley

School of Information Management and Systems

SIMS 257: Database Management

IS 257 – Spring 2004 2004.04.13- SLIDE 2

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)

IS 257 – Spring 2004 2004.04.13- SLIDE 3

Lecture Outline




IS 257 – Spring 2004 2004.04.13- SLIDE 4

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.

IS 257 – Spring 2004 2004.04.13- SLIDE 5

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));

IS 257 – Spring 2004 2004.04.13- SLIDE 6

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.

IS 257 – Spring 2004 2004.04.13- SLIDE 7

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

IS 257 – Spring 2004 2004.04.13- SLIDE 8

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.)

IS 257 – Spring 2004 2004.04.13- SLIDE 9

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 Microsoft’s ODBC

IS 257 – Spring 2004 2004.04.13- SLIDE 10

JDBC

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

Resultset

Statement

Resultset Resultset

Connection

PreparedStatement CallableStatement

DriverManager

Oracle Driver ODBC Driver Postgres Driver

Oracle DB Postgres DBODBC DB

Application

IS 257 – Spring 2004 2004.04.13- SLIDE 11

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;}

IS 257 – Spring 2004 2004.04.13- SLIDE 12

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(

"jdbc:oracle:thin:@dream.sims.berkeley.edu:1521:dev", “mylogin", “myoraclePW");

// Do an SQL statement... Statement stmt = con.createStatement(); ResultSet rs = stmt.executeQuery("SELECT NAME FROM DIVECUST");

IS 257 – Spring 2004 2004.04.13- SLIDE 13

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(System.out);}

}}

IS 257 – Spring 2004 2004.04.13- SLIDE 14

Lecture Outline




IS 257 – Spring 2004 2004.04.13- SLIDE 15

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

IS 257 – Spring 2004 2004.04.13- SLIDE 16

Problem: Heterogeneous Information Sources

“Heterogeneities are everywhere”

Different interfaces Different data representations Duplicate and inconsistent information

PersonalDatabases

Digital Libraries

Scientific DatabasesWorldWideWeb

Slide credit: J. Hammer

IS 257 – Spring 2004 2004.04.13- SLIDE 17

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 PlanningStock Mngmt

...

Suppliers

...Debt Mngmt

Num. Control

...Inventory


IS 257 – Spring 2004 2004.04.13- SLIDE 18

Goal: Unified Access to Data

Integration System

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

WorldWideWeb

Digital Libraries Scientific Databases

PersonalDatabases


IS 257 – Spring 2004 2004.04.13- SLIDE 19

The Traditional Research Approach

Source SourceSource. . .

Integration System

. . .

Metadata

Clients

Wrapper WrapperWrapper

• Query-driven (lazy, on-demand)


IS 257 – Spring 2004 2004.04.13- SLIDE 20

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

• Hasn’t caught on in industry


IS 257 – Spring 2004 2004.04.13- SLIDE 21

The Warehousing Approach

DataDataWarehouseWarehouse

Clients

Source SourceSource. . .

Extractor/Monitor

Integration System

. . .

Metadata

Extractor/Monitor

Extractor/Monitor

• Information integrated in advance

• Stored in WH for direct querying and analysis


IS 257 – Spring 2004 2004.04.13- SLIDE 22

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

• Has caught on in industry


IS 257 – Spring 2004 2004.04.13- SLIDE 23

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


IS 257 – Spring 2004 2004.04.13- SLIDE 24

Data Warehouse EvolutionT

IME

200019951990198519801960 1975

Information-Based Management

DataRevolution

“MiddleAges”

“PrehistoricTimes”

RelationalDatabases

PC’s andSpreadsheets

End-userInterfaces

1st DW Article

DWConfs.

Vendor DWFrameworks

CompanyDWs

“Building theDW”

Inmon (1992)Data Replication

Tools


IS 257 – Spring 2004 2004.04.13- SLIDE 25

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

IS 257 – Spring 2004 2004.04.13- SLIDE 26

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.

IS 257 – Spring 2004 2004.04.13- SLIDE 27

DW Definition…

• Integrated– The data housed in the data warehouse are

defined using consistent• Naming conventions• Formats• Encoding Structures• Related Characteristics

IS 257 – Spring 2004 2004.04.13- SLIDE 28

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

IS 257 – Spring 2004 2004.04.13- SLIDE 29

DW Definition…

• Non-volatile– Data in the data warehouse are loaded and

refreshed from operational systems, but cannot be updated by end-users

IS 257 – Spring 2004 2004.04.13- SLIDE 30

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


IS 257 – Spring 2004 2004.04.13- SLIDE 31

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

IS 257 – Spring 2004 2004.04.13- SLIDE 32

… 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 WalMart– Complete client histories at insurance firm– Stockbroker financial information and portfolios


IS 257 – Spring 2004 2004.04.13- SLIDE 33

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)


IS 257 – Spring 2004 2004.04.13- SLIDE 34

Summary

Operational SystemsEnterpriseModeling

BusinessInformation Guide

DataWarehouse

Catalog

Data WarehousePopulation

DataWarehouse

Business InformationInterface


IS 257 – Spring 2004 2004.04.13- SLIDE 35

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


IS 257 – Spring 2004 2004.04.13- SLIDE 36

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


IS 257 – Spring 2004 2004.04.13- SLIDE 37

Data Warehousing Architecture

IS 257 – Spring 2004 2004.04.13- SLIDE 38

“Ingest”

DataDataWarehouseWarehouse

Clients

Source/ File Source / ExternalSource / DB. . .

Extractor/Monitor

Integration System

. . .

Metadata

Extractor/Monitor

Extractor/Monitor

IS 257 – Spring 2004 2004.04.13- SLIDE 39

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”

Operationalsystems

Informationalsystems

Derived Data

Real-time data

Operationalsystems



IS 257 – Spring 2004 2004.04.13- SLIDE 40

Three-layer Architecture: Conceptual View

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

Derived Data

Real-time data

Operationalsystems


Reconciled DataPhysical Implementationof the Data Warehouse

View level“Particular informational

needs”


IS 257 – Spring 2004 2004.04.13- SLIDE 41

Issues in Data Warehousing

• Warehouse Design

• Extraction– Wrappers, monitors (change detectors)

• Integration– Cleansing & merging

• Warehousing specification & Maintenance

• Optimizations

• Miscellaneous (e.g., evolution)


IS 257 – Spring 2004 2004.04.13- SLIDE 42

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)


IS 257 – Spring 2004 2004.04.13- SLIDE 43

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”


IS 257 – Spring 2004 2004.04.13- SLIDE 44

Wrapper

Converts data and queries from one data model to another

Extends query capabilities for sources with limited capabilities

DataModel

B

DataModel

A

Queries

Data

Queries SourceWrapper


IS 257 – Spring 2004 2004.04.13- SLIDE 45

Wrapper Generation

• Solution 1: Hard code for each source

• Solution 2: Automatic wrapper generation

WrapperWrapper

GeneratorDefinition


IS 257 – Spring 2004 2004.04.13- SLIDE 46

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


IS 257 – Spring 2004 2004.04.13- SLIDE 47

Monitors

• Goal: Detect changes of interest and propagate to integrator

• How?– Triggers– Replication server– Log sniffer– Compare query results– Compare snapshots/dumps


IS 257 – Spring 2004 2004.04.13- SLIDE 48

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.


IS 257 – Spring 2004 2004.04.13- SLIDE 49

Data Cleansing

• Find (& remove) duplicate tuples– e.g., Jane Doe vs. Jane Q. Doe

• Detect inconsistent, wrong data– Attribute values that don’t match

• Patch missing, unreadable data

• Notify sources of errors found


IS 257 – Spring 2004 2004.04.13- SLIDE 50

Warehouse Maintenance

• Warehouse data materialized view– Initial loading– View maintenance

• View maintenance


IS 257 – Spring 2004 2004.04.13- SLIDE 51

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


IS 257 – Spring 2004 2004.04.13- SLIDE 52

Differs from Conventional View Maintenance...

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


IS 257 – Spring 2004 2004.04.13- SLIDE 53

Sales Comp.

Integrator

DataWarehouse

Sale(item,clerk) Emp(clerk,age)

Sold (item,clerk,age)

Sold = Sale Emp


Warehouse Maintenance Anomalies

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

• Simple example

IS 257 – Spring 2004 2004.04.13- SLIDE 54

1. Insert into Emp(Mary,25), notify integrator

2. Insert into Sale (Computer,Mary), notify integrator

3. (1) integrator adds Sale (Mary,25)

4. (2) integrator adds (Computer,Mary) Emp

5. View incorrect (duplicate tuple)

Sales Comp.

Integrator

DataWarehouse

Sale(item,clerk) Emp(clerk,age)

Sold (item,clerk,age)


Warehouse Maintenance Anomalies

IS 257 – Spring 2004 2004.04.13- SLIDE 55


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

IS 257 – Spring 2004 2004.04.13- SLIDE 56

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

Self-Maintainability: Examples


IS 257 – Spring 2004 2004.04.13- SLIDE 57

Self-Maintainability: Examples

• Deletes from SaleDelete 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


IS 257 – Spring 2004 2004.04.13- SLIDE 58

Partial Self-Maintainability

• Avoid (but don’t prohibit) going to sourcesSold=Sale(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 ...


IS 257 – Spring 2004 2004.04.13- SLIDE 59

Warehouse Specification (ideally)

Extractor/Monitor

Extractor/Monitor

Extractor/Monitor

Integrator

Warehouse

...

Metadata

Warehouse Configuration

Module

View Definitions

Integrationrules

ChangeDetection

Requirements


IS 257 – Spring 2004 2004.04.13- SLIDE 60

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


IS 257 – Spring 2004 2004.04.13- SLIDE 61

Additional Research Issues

• Historical views of non-historical data

• Expiring outdated information

• Crash recovery

• Addition and removal of information sources– Schema evolution


IS 257 – Spring 2004 2004.04.13- SLIDE 62

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.

2004.04.13- slide 1is 257 – spring 2004 data warehousing university of california, berkeley school...

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