Data Warehousing University of California Berkeley School of
Data Warehousing University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management IS 257 – Spring 2004. 04. 13 - SLIDE 1
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. 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. 04. 13 - SLIDE 3
Postgre. SQL Extensibility • Postgres is extensible because its operation is catalogdriven – 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. 04. 13 - SLIDE 4
A more complex function • To illustrate a simple SQL function, consider the following, which might be used to debit a bank account: create function TP 1 (int 4, float 8) returns int 4 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 = TP 1( 17, 100. 0)); IS 257 – Spring 2004. 04. 13 - SLIDE 5
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. 04. 13 - SLIDE 6
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. 04. 13 - SLIDE 7
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. 04. 13 - SLIDE 8
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. 04. 13 - SLIDE 9
JDBC • Provides a standard set of interfaces for any DBMS with a JDBC driver – using SQL to specify the databases operations. Resultset Statement Prepared. Statement Callable. Statement Application Connection Driver. Manager Oracle Driver ODBC Driver Postgres Driver Oracle DB ODBC DB Postgres DB IS 257 – Spring 2004. 04. 13 - SLIDE 10
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. for. Name("jdbc. oracle. thin"); Driver. Manager. register. Driver(new Oracle. Driver()); } catch (SQLException e) { System. out. println("Unable to load driver Class"); return; } IS 257 – Spring 2004. 04. 13 - SLIDE 11
JDBC Simple Java Impl. try { //All DB access is within the try/catch block. . . // make a connection to ORACLE on Dream Connection con = Driver. Manager. get. Connection( "jdbc: oracle: thin: @dream. sims. berkeley. edu: 1521: dev", “mylogin", “myoracle. PW"); // Do an SQL statement. . . Statement stmt = con. create. Statement(); Result. Set rs = stmt. execute. Query("SELECT NAME FROM DIVECUST"); IS 257 – Spring 2004. 04. 13 - SLIDE 12
JDBC Simple Java Impl. // show the Results. . . while(rs. next()) { System. out. println(rs. get. String("NAME")); } // Release the database resources. . . rs. close(); stmt. close(); con. close(); } catch (SQLException se) { // inform user of errors. . . System. out. println("SQL Exception: " + se. get. Message()); se. print. Stack. Trace(System. out); } } } IS 257 – Spring 2004. 04. 13 - SLIDE 13
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. 04. 13 - SLIDE 14
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. 04. 13 - SLIDE 15
Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” Personal Databases Scientific Databases Digital Libraries Different interfaces p Different data representations p Duplicate and inconsistent information p IS 257 – Spring 2004 World Wide Web Slide credit: J. Hammer 2004. 13 - SLIDE 16
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 IS 257 – Spring 2004 Finance Manufacturing . . . Slide credit: J. Hammer 2004. 13 - SLIDE 17
Goal: Unified Access to Data Integration System World Wide Web Digital Libraries Scientific Databases Personal Databases • Collects and combines information • Provides integrated view, uniform user interface • Supports sharing Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 18
The Traditional Research Approach • Query-driven (lazy, on-demand) Clients Metadata Integration System . . . Wrapper Source Wrapper . . . Source Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 19
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 IS 257 – Spring 2004. 04. 13 - SLIDE 20
The Warehousing Approach • Information integrated in advance • Stored in WH for direct querying and analysis Extractor/ Monitor Source IS 257 – Spring 2004 Clients Data Warehouse Integration System Metadata . . . Extractor/ Monitor Source Extractor/ Monitor . . . Source Slide credit: J. Hammer 2004. 13 - SLIDE 21
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 IS 257 – Spring 2004. 04. 13 - SLIDE 22
Not Either-Or Decision • Query-driven approach still better for – Rapidly changing information sources – Truly vast amounts of data from large numbers of sources – Clients with unpredictable needs Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 23
Data Warehouse Evolution Relational Databases 1960 1975 Company DWs 1980 PC’s and Spreadsheets End-user Interfaces 1985 1990 Data Replication Tools 1995 2000 Information“Middle Data Based Revolution Ages” Management 1 st DW Article DW Confs. TIME “Prehistoric Times” “Building the DW” Inmon (1992) Vendor DW Frameworks Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 24
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. 04. 13 - SLIDE 25
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. 04. 13 - SLIDE 26
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. 04. 13 - SLIDE 27
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. 04. 13 - SLIDE 28
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. 04. 13 - SLIDE 29
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 use it in a business context. ” • -- Barry Devlin, IBM Consultant IS 257 – Spring 2004. 04. 1330 Slide credit: J. SLIDE 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 transactionoriented db • User interface aimed at executive decision makers and analysts IS 257 – Spring 2004. 04. 13 - SLIDE 31
… 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 Wal. Mart – Complete client histories at insurance firm – Stockbroker financial information and portfolios Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 32
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 IS 257 – Spring 2004. 04. 13 - SLIDE 33
Summary Business Information Guide Data Warehouse Catalog Business Information Interface Data Warehouse Population Enterprise Modeling Operational Systems Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 34
Warehousing and Industry • Warehousing is big business – $2 billion in 1995 – $3. 5 billion in early 1997 – Predicted: $8 billion in 1998 [Metagroup] • Wal. Mart has largest warehouse – 900 -CPU, 2, 700 disk, 23 TB Teradata system – ~7 TB in warehouse – 40 -50 GB per day Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 35
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 IS 257 – Spring 2004. 04. 13 - SLIDE 36
Data Warehousing Architecture IS 257 – Spring 2004. 04. 13 - SLIDE 37
“Ingest” Clients Data Warehouse Integration System Metadata . . . Extractor/ Monitor Source/ File IS 257 – Spring 2004 Extractor/ Monitor Source / DB Extractor/ Monitor . . . Source / External 2004. 13 - SLIDE 38
Data Warehouse Architectures: Conceptual View • Single-layer Operational systems Informational systems – Every data element is stored once only “Real-time data” – Virtual warehouse • Two-layer – Real-time + derived data – Most commonly used approach in – industry today Operational systems Informational systems Derived Data Real-time data Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 39
Three-layer Architecture: Conceptual View • Transformation of real-time data to derived data really requires two steps Operational systems Informational systems Derived Data Reconciled Data View level “Particular informational needs” Physical Implementation of the Data Warehouse Real-time data Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 40
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 IS 257 – Spring 2004. 04. 13 - SLIDE 41
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 IS 257 – Spring 2004. 04. 13 - SLIDE 42
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 IS 257 – Spring 2004. 04. 13 - SLIDE 43
Wrapper p Converts data and queries from one data model to another Data Model A Queries Data Model B p Extends query capabilities for sources with limited capabilities Queries Wrapper Source Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 44
Wrapper Generation • Solution 1: Hard code for each source • Solution 2: Automatic wrapper generation Wrapper Generator Definition Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 45
Data Transformations • Convert data to uniformat – Byte ordering, string termination – Internal layout • Remove, add & reorder attributes – Add key – Add data to get history • Sort tuples Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 46
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 IS 257 – Spring 2004. 04. 13 - SLIDE 47
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 IS 257 – Spring 2004. 04. 13 - SLIDE 48
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 IS 257 – Spring 2004. 04. 13 - SLIDE 49
Warehouse Maintenance • Warehouse data materialized view – Initial loading – View maintenance • View maintenance Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 50
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 IS 257 – Spring 2004. 04. 13 - SLIDE 51
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 IS 257 – Spring 2004. 04. 13 - SLIDE 52
Warehouse Maintenance Anomalies • Materialized view maintenance in loosely coupled, non-transactional environment • Simple example Data Warehouse Sold (item, clerk, age) Sold = Sale Emp Integrator Sales Sale(item, clerk) IS 257 – Spring 2004 Comp. Emp(clerk, age) Slide credit: J. Hammer 2004. 13 - SLIDE 53
Warehouse Maintenance Anomalies Data Warehouse Sold (item, clerk, age) Integrator Sales Sale(item, clerk) Comp. Emp(clerk, age) 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) Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 54
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 credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 55
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 Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 56
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 IS 257 – Spring 2004. 04. 13 - SLIDE 57
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 IS 257 – Spring 2004. 04. 13 - SLIDE 58
Warehouse Specification (ideally) View Definitions Warehouse Configuration Module Integration rules Warehouse Change Detection Requirements Integrator Extractor/ Monitor Metadata Extractor/ Monitor . . . Slide credit: J. Hammer IS 257 – Spring 2004. 04. 13 - SLIDE 59
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 IS 257 – Spring 2004. 04. 13 - SLIDE 60
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 IS 257 – Spring 2004. 04. 13 - SLIDE 61
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 4 th Intl. CIKM Conf. , 1995. • Chaudhuri, S. , Dayal, U. , “An Overview of Data Warehousing and OLAP Technology. ” ACM SIGMOD Record, March 1997. IS 257 – Spring 2004. 04. 13 - SLIDE 62
- Slides: 62