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Notes11 - Data Warehousing Overview CS245 Notes 11 Hector...

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Data Warehousing Overview CS245 Notes 11 Hector Garcia-Molina Stanford University CS 245 1 Notes11
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CS 245 Notes11 2 Warehousing Growing industry: $8 billion in 1998 Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB Teradata system Lots of buzzwords, hype slice & dice, rollup, MOLAP, pivot, ...
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CS 245 Notes11 3 Outline What is a data warehouse? Why a warehouse? Models & operations Implementing a warehouse
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CS 245 Notes11 4 What is a Warehouse? Collection of diverse data subject oriented aimed at executive, decision maker often a copy of operational data with value-added data (e.g., summaries, history) integrated time-varying non-volatile more
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CS 245 Notes11 5 What is a Warehouse? Collection of tools gathering data cleansing, integrating, ... querying, reporting, analysis data mining monitoring, administering warehouse
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CS 245 Notes11 6 Warehouse Architecture Client Client Warehouse Source Source Source Query & Analysis Integration Metadata
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CS 245 Notes11 7 Motivating Examples Forecasting Comparing performance of units Monitoring, detecting fraud Visualization
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CS 245 Notes11 8 Why a Warehouse? Two Approaches: Query-Driven (Lazy) Warehouse (Eager) Source Source ?
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CS 245 Notes11 9 Query-Driven Approach Client Client Wrapper Wrapper Wrapper Mediator Source Source Source
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CS 245 Notes11 10 Advantages of Warehousing High query performance Queries not visible outside warehouse Local processing at sources unaffected Can operate when sources unavailable Can query data not stored in a DBMS Extra information at warehouse Modify, summarize (store aggregates) Add historical information
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CS 245 Notes11 11 Advantages of Query-Driven No need to copy data less storage no need to purchase data More up-to-date data Query needs can be unknown Only query interface needed at sources May be less draining on sources
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CS 245 Notes11 12 OLTP vs. OLAP OLTP: On Line Transaction Processing Describes processing at operational sites OLAP: On Line Analytical Processing Describes processing at warehouse
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CS 245 Notes11 13 OLTP vs. OLAP Mostly updates Many small transactions Mb-Tb of data Raw data Clerical users Up-to-date data Consistency, recoverability critical Mostly reads Queries long, complex Gb-Tb of data Summarized, consolidated data Decision-makers, analysts as users OLTP OLAP
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CS 245 Notes11 14 Warehouse Models & Operators Data Models relations stars & snowflakes cubes Operators slice & dice roll-up, drill down pivoting other
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CS 245 Notes11 15 Star customer custId name address city 53 joe 10 main sfo 81 fred 12 main sfo 111 sally 80 willow la product prodId name price p1 bolt 10 p2 nut 5 store storeId city c1 nyc c2 sfo c3 la sale oderId date custId prodId storeId qty amt o100 1/7/97 53 p1 c1 1 12 o102 2/7/97 53 p2 c1 2 11 105 3/8/97 111 p1 c3 5 50
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CS 245 Notes11 16 Star Schema sale orderId date custId prodId storeId qty amt customer custId name address city product prodId name price store storeId city
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