Introducing dwh 2010

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Rien Matthijsse

Presentation given for 60 participants of The Future Group

Educación

Introducing DATA WAREHOUSING 2010 A tour of definition

RienMatthijsse 1st computer: 1976 ICT: 1986 ICT freelancer: 1998 TGF’er: April 2009

Business Intelligence Data warehouse ETL Hardware RDBMS

Talk the talk @ the right place @ the right person

Product Group Product Customer CustomerGroup Product Line Customer Variety Day Period Week Month Year Per Product Group Howmuch Revenue Qty Ordered ….. Country Country In the last month Per Country

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BUS105 Business Information Systems Workshop Week 3 Small and big Data Collection, Storage and Management in Relation to Information Systems Copyright Notice COPYRIGHT COMMONWEALTH OF AUSTRALIA Copyright Regulations 1969 WARNING This material has been reproduced and communicated to you by or on behalf of Kaplan Higher Education pursuant to Part VB of the Copyright Act 1968 (the Act). The material in this communication may be subject to copyright under the Act. Any further reproduction or communication of this material by you may be the subject of copyright protection under the Act. Do not remove this notice 2 Lesson Learning Outcomes 1 Review different types of data 2 Contrast small and big data collection 3 Learn about data storage and management 4 Examine business case studies in relation to the type of data requirements for particular information systems Splunk: Slicing Data for Domino’s Pizza • Watch the video on how Splunk is helping to improve Domino’s business functions https://www.youtube.com/watch?v=LXMjN6kVmUY Q: What was the big event that occurred in the US that required many pizza orders? https://www.youtube.com/watch?v=LXMjN6kVmUY • Raw data (primary data) – Numbers, words, symbols collected from a source – Not cleaned or processed – may have errors or outliers • Metadata – Data that provides information about other data – “Metadata explains the origin, purpose, time, geographic location, creator, access, and terms of use of the data.” https://data.library.arizona.edu/data-management-tips/data-documentation-and-metadata Glossary 1 LO1 https://data.library.arizona.edu/data-management-tips/data-documentation-and-metadata • Metadata from a pdf file Metadata Example Glossary 2 LO1 • Structured data is formatted for use, has a well-defined data structure, generally stored in rows and columns - e.g. age (in years), first name (text), address (text), income ($), etc. We will learn more about this in the relational database section of the slides. • Semi-structured data has some structure - e.g. CSV files with comma separated data. XML and JavaScript Object Notation, JSON, documents used to exchange data to/from a web server • Parse means to analyse (a string or text) into logical syntactic components. EMC Education Services (Eds.) 2015, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons, Indianapolis, US. https://www.google.com/search?q=parsing+definition&ie=&oe= https://en.wikipedia.org/wiki/JSON https://www.google.com/search?q=parsing+definition&ie=&oe Glossary 3 LO1 • Quasi-structured data textual data which has various formats and takes effort to handle and analyse – e.g. web clickstream data • Unstructured data has no predefined data model, not organised, may have multiple types of data - e.g. data from thermostats, sensors, home electronic devices, cars, images and soun ...

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