SLIDE 1IS 257 – Spring 2004 Data Warehousing University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management

SLIDE 2IS 257 – Spring 2004 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)

SLIDE 3IS 257 – Spring 2004 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)

SLIDE 4IS 257 – Spring 2004 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.

SLIDE 5IS 257 – Spring 2004 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 $ as follows: select (x = TP1( 17,100.0));

SLIDE 6IS 257 – Spring 2004 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.

SLIDE 7IS 257 – Spring 2004 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

SLIDE 8IS 257 – Spring 2004 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.)

SLIDE 9IS 257 – Spring 2004 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

SLIDE 10IS 257 – Spring 2004 JDBC Provides a standard set of interfaces for any DBMS with a JDBC driver – using SQL to specify the databases operations. Resultset Statement Resultset Connection PreparedStatementCallableStatement DriverManager Oracle Driver ODBC DriverPostgres Driver Oracle DBPostgres DBODBC DB Application

SLIDE 11IS 257 – Spring 2004 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; }

SLIDE 12IS 257 – Spring 2004 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( “mylogin", “myoraclePW"); // Do an SQL statement... Statement stmt = con.createStatement(); ResultSet rs = stmt.executeQuery("SELECT NAME FROM DIVECUST");

SLIDE 13IS 257 – Spring 2004 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); }

SLIDE 14IS 257 – Spring 2004 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)

SLIDE 15IS 257 – Spring 2004 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

SLIDE 16IS 257 – Spring 2004 Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” p Different interfaces p Different data representations p Duplicate and inconsistent information Personal Databases Digital Libraries Scientific Databases World Wide Web Slide credit: J. Hammer

SLIDE 17IS 257 – Spring 2004 Problem: Data Management in Large Enterprises Vertical fragmentation of informational systems (vertical stove pipes) Result of application (user)-driven development of operational systems Sales AdministrationFinanceManufacturing... Sales Planning Stock Mngmt... Suppliers... Debt Mngmt Num. Control... Inventory Slide credit: J. Hammer

SLIDE 18IS 257 – Spring 2004 Goal: Unified Access to Data Integration System Collects and combines information Provides integrated view, uniform user interface Supports sharing World Wide Web Digital LibrariesScientific Databases Personal Databases Slide credit: J. Hammer

SLIDE 19IS 257 – Spring 2004 The Traditional Research Approach Source... Integration System... Metadata Clients Wrapper Query-driven (lazy, on-demand) Slide credit: J. Hammer

SLIDE 20IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 21IS 257 – Spring 2004 The Warehousing ApproachDataWarehouse Clients Source... Extractor/ Monitor Integration System... Metadata Extractor/ Monitor Extractor/ Monitor Information integrated in advance Stored in WH for direct querying and analysis Slide credit: J. Hammer

SLIDE 22IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 23IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 24IS 257 – Spring 2004 Data Warehouse Evolution TIME Information- Based Management Data Revolution “Middle Ages” “Prehistoric Times” Relational Databases PC’s and Spreadsheets End-user Interfaces 1st DW Article DW Confs. Vendor DW Frameworks Company DWs “Building the DW” Inmon (1992) Data Replication Tools Slide credit: J. Hammer

SLIDE 25IS 257 – Spring 2004 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

SLIDE 26IS 257 – Spring 2004 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.

SLIDE 27IS 257 – Spring 2004 DW Definition… Integrated –The data housed in the data warehouse are defined using consistent Naming conventions Formats Encoding Structures Related Characteristics

SLIDE 28IS 257 – Spring 2004 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

SLIDE 29IS 257 – Spring 2004 DW Definition… Non-volatile –Data in the data warehouse are loaded and refreshed from operational systems, but cannot be updated by end-users

SLIDE 30IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 31IS 257 – Spring 2004 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

SLIDE 32IS 257 – Spring 2004 … 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 Slide credit: J. Hammer

SLIDE 33IS 257 – Spring 2004 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) Slide credit: J. Hammer

SLIDE 34IS 257 – Spring 2004 Summary Operational Systems Enterprise Modeling Business Information Guide Data Warehouse Catalog Data Warehouse Population Data Warehouse Business Information Interface Slide credit: J. Hammer

SLIDE 35IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 36IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 37IS 257 – Spring 2004 Data Warehousing Architecture

SLIDE 38IS 257 – Spring 2004 “Ingest”DataWarehouse Clients Source/ FileSource / ExternalSource / DB... Extractor/ Monitor Integration System... Metadata Extractor/ Monitor Extractor/ Monitor

SLIDE 39IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 40IS 257 – Spring 2004 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” Slide credit: J. Hammer

SLIDE 41IS 257 – Spring 2004 Issues in Data Warehousing Warehouse Design Extraction –Wrappers, monitors (change detectors) Integration –Cleansing & merging Warehousing specification & Maintenance Optimizations Miscellaneous (e.g., evolution) Slide credit: J. Hammer

SLIDE 42IS 257 – Spring 2004 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) Slide credit: J. Hammer

SLIDE 43IS 257 – Spring 2004 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” Slide credit: J. Hammer

SLIDE 44IS 257 – Spring 2004 Wrapper pConverts data and queries from one data model to another pExtends query capabilities for sources with limited capabilities Data Model B Data Model A Queries Data Queries Source Wrapper Slide credit: J. Hammer

SLIDE 45IS 257 – Spring 2004 Wrapper Generation Solution 1: Hard code for each source Solution 2: Automatic wrapper generation Wrapper Generator Definition Slide credit: J. Hammer

SLIDE 46IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 47IS 257 – Spring 2004 Monitors Goal: Detect changes of interest and propagate to integrator How? –Triggers –Replication server –Log sniffer –Compare query results –Compare snapshots/dumps Slide credit: J. Hammer

SLIDE 48IS 257 – Spring 2004 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. Slide credit: J. Hammer

SLIDE 49IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 50IS 257 – Spring 2004 Warehouse Maintenance Warehouse data  materialized view –Initial loading –View maintenance View maintenance Slide credit: J. Hammer

SLIDE 51IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 52IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 53IS 257 – Spring 2004 SalesComp. Integrator Data Warehouse Sale(item,clerk)Emp(clerk,age) Sold (item,clerk,age) Sold = Sale Emp Slide credit: J. Hammer Warehouse Maintenance Anomalies Materialized view maintenance in loosely coupled, non-transactional environment Simple example

SLIDE 54IS 257 – Spring 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) SalesComp. Integrator Data Warehouse Sale(item,clerk)Emp(clerk,age) Sold (item,clerk,age) Slide credit: J. Hammer Warehouse Maintenance Anomalies

SLIDE 55IS 257 – Spring 2004 Slide credit: J. Hammer 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

SLIDE 56IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 57IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 58IS 257 – Spring 2004 Partial Self-Maintainability Avoid (but don’t prohibit) going to sources Sold=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... Slide credit: J. Hammer

SLIDE 59IS 257 – Spring 2004 Warehouse Specification (ideally) Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor Integrator Warehouse... Metadata Warehouse Configuration Module View Definitions Integration rules Change Detection Requirements Slide credit: J. Hammer

SLIDE 60IS 257 – Spring 2004 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 Slide credit: J. Hammer

SLIDE 61IS 257 – Spring 2004 Additional Research Issues Historical views of non-historical data Expiring outdated information Crash recovery Addition and removal of information sources –Schema evolution Slide credit: J. Hammer

SLIDE 62IS 257 – Spring 2004 More Information on DW Agosta, Lou, The Essential Guide to Data Warehousing. Prentise Hall PTR, Devlin, Barry, Data Warehouse, from Architecture to Implementation. Addison-Wesley, Inmon, W.H., Building the Data Warehouse. John Wiley, Widom, J., “Research Problems in Data Warehousing.” Proc. of the 4th Intl. CIKM Conf., Chaudhuri, S., Dayal, U., “An Overview of Data Warehousing and OLAP Technology.” ACM SIGMOD Record, March 1997.