L7. bigdata - Big Data Lecture 4 Big Data Big Data...

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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.1 Lecture 4 Big Data EE4221 Cloud Computing Systems Nov. 5., 2015 Dr. Anna Ruokonen City University of Hong Kong
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.2 Agenda 1 Big Data 2 Analytics 3 noSQL 4 Hadoop 5 Map Reduce revisited 6 Analytics Tools
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.3 Big Data Characteristics Too large data sets for traditional database management tools or data processing applications Scientific applications, social networks, search the web, customer mining, etc. Need for parallel and distributed computing Raw, unstructured, or semi-structured Also, needs for transferring huge data in the network
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.4 Big Data Definitions Three Vs: Volume, Variety, and Velocity In addition to large volume, is often in unstructured form, rather a changing stream than a batch type data Data can be analyzed, for example, to produce economic indicators on financial, unemployment and health related issues Analytics for big data in rest and analytics for big data in motion operating on data stored in a database or file system to analysis and manipulation of data streams
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.5 Big Data Definitions Two different target usages for big data analytics can be identified providing the developers the runtime and development environment for advanced applications providing tools for business users to analyze big data Data visualization The main objective is to represent knowledge more intuitively and effectively by using different graphs. For example, eBay Tableau is used for transforming large data sets into intuitive pictures Important data sources: social media, web data, smart grid and sensors, RFID and barcodes, GPS, stock feeds, and mobile usage.
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.6 Data Analytics Lifecycle
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.7 (1) Discovery Learn the business domain and relevant history Assess resources: people, technology, data Formulate initial hypothesis Determine criteria for success and failure
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Big Data Big Data Analytics noSQL Hadoop Map Reduce revisited Analytics Tools 4.8 (2) Data preparation Extract the data Data cleaning and preprocessing Make sure you have enough good quality data Study your data
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