2011-11-30-NonTraditional-Part1- NoSQL

2011-11-30-NonTraditional-Part1- NoSQL - 11/30/2011...

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11/30/2011 1 Non Traditional Data Management Infrastructures CS4320 Fall 2011 Slides courtesy of Raghotham Murthy (Stanford), Adina Cr ă iniceanu (Naval Academy), and Markus Böhning (Berkeley) Agenda OLAP and data warehousing The NoSQL movement Alternative solutions for OLTP and OLAP applications OLAP MapReduce Hive PIG OLTP Amazon Dynamo Google Big Table P2P Systems Chord Agenda OLAP and data warehousing Overview of data warehousing Dimensional Modeling Online Analytical Processing The NoSQL movement Alternative solutions for OLTP and OLAP applications OLAP MapReduce Hive PIG OLTP Amazon Dynamo Google Big Table P2P Systems Chord
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11/30/2011 2 4 Data Driven Web Application Maintain customer shopping carts Present available products Confirm order Process incoming requests (product search, orders,…) Customer service Analyze business performance Update product availability Send orders to warehouse Update order status Large amount of complex data, concurrent query and update processing Many users, geographical y distributed Read data Actions can change data Access through simple web interface (browser) Examples: eBay, Amazon.com, flight reservation systems, CMS,… 5 Data Warehouse: Motivation Large retailer Several databases: inventory, personnel, sales etc. High volume of updates Management requirements Efficient support for decision making Comprehensive view of all aspects of an enterprise Trends, summaries, analysis of historical data Information from several departments Why not use operational systems? 6 Motivation (contd.) Integrate data from diverse sources Common schema Semantic mismatches (currency, naming, normalization, databases structure) Clean data (missing values, inconsistencies) Accumulate historical data Not relevant for operational databases Efficient analysis Complex queries versus frequent updates
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11/30/2011 3 7 Terminology OLTP (Online Transaction Processing) DSS (Decision Support System) DW (Data Warehouse) OLAP (Online Analytical Processing) 8 From OLTP to the Data Warehouse Traditionally, database systems stored data relevant to current business processes Old data was archived or purged A database stores the current snapshot of the business: Current customers with current addresses Current inventory Current orders Current account balance 9 The Data Warehouse Historical collection of all relevant data for analysis purposes Examples: Current customers versus all customers Current orders versus history of all orders Current inventory versus history of all shipments Stores information that might be useless for the operational part of a business
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11/30/2011 4 10 OLTP Architecture OLTP DBMSs Clients Cash Register Product Purchase Inventory Update 11 DW Architecture Information Sources Data Warehouse Server OLAP Servers Clients OLTP DBMSs
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2011-11-30-NonTraditional-Part1- NoSQL - 11/30/2011...

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