Data Warehousing

Data Warehousing University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management

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) Lecture Outline

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.

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));

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.

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

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.)

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

JDBC Resultset Resultset Resultset Statement PreparedStatement CallableStatement Connection Application DriverManager Oracle Driver ODBC Driver Postgres Driver Oracle DB ODBC DB Postgres DB • Provides a standard set of interfaces for any DBMS with a JDBC driver – using SQL to specify the databases operations.

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; }

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");

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); } } }

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) Lecture Outline

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

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

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 ... Slide credit: J. Hammer

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

The Traditional Research Approach • Query-driven (lazy, on-demand) Clients Metadata Integration System . . . Wrapper Wrapper Wrapper . . . Source Source Source Slide credit: J. Hammer

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

The Warehousing Approach Clients Data Warehouse Metadata Integration System . . . Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor . . . Source Source Source • Information integrated in advance • Stored in WH for direct querying and analysis Slide credit: J. Hammer

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

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

Data Warehouse Evolution “Building the DW” Inmon (1992) Data Replication Tools Relational Databases Company DWs 1960 1975 1980 1985 1990 1995 2000 Information- Based Management Data Revolution “Prehistoric Times” “Middle Ages” TIME PC’s and Spreadsheets End-user Interfaces 1st DW Article DW Confs. Vendor DW Frameworks Slide credit: J. Hammer

“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 What is a Data Warehouse?

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.

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

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

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

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

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

… 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

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) Warehouse is a Specialized DB Slide credit: J. Hammer

Summary Business Information Guide Business Information Interface Data Warehouse Data Warehouse Catalog Data Warehouse Population Enterprise Modeling Operational Systems Slide credit: J. Hammer

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

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

Data Warehousing Architecture

“Ingest” Clients Data Warehouse Metadata Integration System . . . Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor . . . Source/ File Source / DB Source / External

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

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

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

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

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

Wrapper Data Model B Wrapper Source • Converts data and queries from one data model to another Queries Data Model A Data • Extends query capabilities for sources with limited capabilities Queries Slide credit: J. Hammer

Wrapper Generation • Solution 1: Hard code for each source • Solution 2: Automatic wrapper generation Wrapper Generator Definition Wrapper Slide credit: J. Hammer

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

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

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

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

Warehouse Maintenance • Warehouse data  materialized view • Initial loading • View maintenance • View maintenance Slide credit: J. Hammer

Data Warehousing