![Data Warehousing/Mining 17
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](https://image.slidesharecdn.com/dwintro-221121135529-67a11ba6/85/DWIntro-ppt-17-320.jpg)
DWIntro.ppt
- 1. Data Warehousing/Mining 1 Data Warehousing/Mining Comp 150 Data Warehousing Introduction (not in book) Instructor: Dan Hebert
- 2. Data Warehousing/Mining 2 Outline of Lecture Data Warehousing and Information Integration Brief History of Data Warehousing What is a Data Warehouse? Types of Data and Their Uses Data Warehouse Architectures Issues in Data Warehousing
- 3. Data Warehousing/Mining 3 Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” Different interfaces Different data representations Duplicate and inconsistent information Personal Databases Digital Libraries Scientific Databases World Wide Web
- 4. Data Warehousing/Mining 4 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 Planning Stock Mngmt ... Suppliers ... Debt Mngmt Num. Control ... Inventory
- 5. Data Warehousing/Mining 5 Goal: Unified Access to Data Integration System • Collects and combines information • Provides integrated view, uniform user interface • Supports sharing World Wide Web Digital Libraries Scientific Databases Personal Databases
- 6. Data Warehousing/Mining 6 The Traditional Research Approach Source Source Source . . . Integration System . . . Metadata Clients Wrapper Wrapper Wrapper Query-driven (lazy, on-demand)
- 7. Data Warehousing/Mining 7 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
- 8. Data Warehousing/Mining 8 The Warehousing Approach Data Warehouse Clients Source Source Source . . . Extractor/ Monitor Integration System . . . Metadata Extractor/ Monitor Extractor/ Monitor Information integrated in advance Stored in wh for direct querying and analysis
- 9. Data Warehousing/Mining 9 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
- 10. Data Warehousing/Mining 10 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
- 11. Data Warehousing/Mining 11 Data Warehouse Evolution TIME 2000 1995 1990 1985 1980 1960 1975 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
- 12. Data Warehousing/Mining 12 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
- 13. Data Warehousing/Mining 13 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
- 14. Data Warehousing/Mining 14 A Data Warehouse is... (continued) 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
- 15. Data Warehousing/Mining 15 Summary Operational Systems Enterprise Modeling Business Information Guide Data Warehouse Catalog Data Warehouse Population Data Warehouse Business Information Interface
- 16. Data Warehousing/Mining 16 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)
- 17. Data Warehousing/Mining 17 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
- 18. Data Warehousing/Mining 18 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
- 19. Data Warehousing/Mining 19 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
- 20. Data Warehousing/Mining 20 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”
- 21. Data Warehousing/Mining 21 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)
- 22. Data Warehousing/Mining 22 Issues in Data Warehousing Warehouse Design Extraction – Wrappers, monitors (change detectors) Integration – Cleansing & merging Warehousing specification & Maintenance Optimizations Miscellaneous (e.g., evolution)
- 23. Data Warehousing/Mining 23 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”
- 24. Data Warehousing/Mining 24 Warehouse Architecture Source Source Source Extractor/ Monitor Extractor/ Monitor Extractor/ Monitor Integrator Warehouse Query & Analysis Client Client ... Metadata
- 25. Data Warehousing/Mining 25 Issues (1) Warehouse uses relational data model or multi- dimensional data model (e.g., data cube) On the other hand, source types – Relational, OO, hierarchical, legacy – Semistructured: flat file, WWW How do we get the data out?
- 26. Data Warehousing/Mining 26 Issues (2) Warehouse must be kept current in light of changes to underlying sources How do we detect updates in sources?
- 27. Data Warehousing/Mining 27 Wrapper Converts data and queries from one data model to another Extends query capabilities for sources with limited capabilities Data Model B Data Model A Queries Data Queries Source Wrapper
- 28. Data Warehousing/Mining 28 Wrapper Generation Solution 1: Hard code for each source Solution 2: Automatic wrapper generation Wrapper Wrapper Generator Definition
- 29. Data Warehousing/Mining 29 Wrapper Approach Source-specific adapter (a.k.a. wrapper, translator) “Thickness” of adapter depends on source – Data model used (e.g. rel. schema vs. unstructured) – Interface (i.e., query language, API) – Active capabilities (i.e., triggers) – Degree of autonomy (e.g., same owner & modifiable vs. controlled by external entity & no changes possible) – Cooperation (e.g., friendly vs. uncooperative)
- 30. Data Warehousing/Mining 30 Routine When... Many tools for dealing with “standard situations” – Standard sources with full/many capabilities e.g., most commercial DBMSs, all ODBC-compliant sources – Standard interactions e.g., pass-through queries, extraction from rel. tables, replication – Cooperative sources or sources under our control Tools – Replication tools, ODBC, report writers, third-party “wrappers”
- 31. Data Warehousing/Mining 31 Not So Routine When... “Non-standard situations” – Unstructured or semistructured sources with little or no explicit schema – Uncooperative sources – Sources with limited capabilities (e.g., legacy sources, WWW) Few commercial tools Mostly research
- 32. Data Warehousing/Mining 32 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
- 33. Data Warehousing/Mining 33 Monitors Goal: Detect changes of interest and propagate to integrator How? – Triggers – Replication server – Log sniffer – Compare query results – Compare snapshots/dumps
- 34. Data Warehousing/Mining 34 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.
- 35. Data Warehousing/Mining 35 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
Editor's Notes
- #2: The slides for this text are organized into several modules. Each lecture contains about enough material for a 1.25 hour class period. (The time estimate is very approximate--it will vary with the instructor, and lectures also differ in length; so use this as a rough guideline.) This lecture is the first of two in Module (1). Module (1): Introduction (DBMS, Relational Model) Module (2): Storage and File Organizations (Disks, Buffering, Indexes) Module (3): Database Concepts (Relational Queries, DDL/ICs, Views and Security) Module (4): Relational Implementation (Query Evaluation, Optimization) Module (5): Database Design (ER Model, Normalization, Physical Design, Tuning) Module (6): Transaction Processing (Concurrency Control, Recovery) Module (7): Advanced Topics