Big data – An Introduction, July 2013
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This document provides an overview of big data, including its definition, sources, databases, and analytics. It defines big data as large datasets greater than terabytes in size that are increasingly being collected from various sources such as science, social media, government and more. It notes that most data is unstructured. It also discusses the evolution of databases from relational SQL databases to non-relational NoSQL databases and Hadoop. Finally, it outlines the major tools and technologies used for big data analytics, including MapReduce, Hadoop, and machine learning.
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Big data – An Introduction, July 2013
- 1. Making Sense out of Big Data Peter Morgan - July 2013
- 2. Table of Contents 1. Definition and Overview 2. Data Sources 3. Databases 4. Data Analytics Glossary References 2
- 3. 1. Definition and Overview 3
- 4. What is big data? More and more data is being collected and stored each day 4
- 5. Four main components • Data – Structured and unstructured • Databases – Proprietary and open source • Query language – Querying the database • Analytics – Analysing the data 5
- 6. How big is big? • Large data sets – Greater than 1,000 Terabytes? (1 Petabyte) – 1,000,000 Terabytes? (1 Exabyte) • Excel 2013 can have 1,048,576 rows by 16,384 columns – About 10 Gigabyte of data • Only going to get bigger – 90% of all data produced in the past two years ! – Rate is increasing • Recall – Giga = 10⁹ – Tera = 10¹² – Peta = 10¹⁵ – Exa = 10¹⁸ 6
- 9. Where does the data come from? • Science – particle, astrophysics • Industry – oil, finance, telecom – Actually all verticals • Social – Facebook, LinkedIn, Twitter • Medicine – genome, neuroscience • Government – census, education, police • Sports – statistics • Environment – weather, sensors 9
- 10. Unstructured Data • 80% of data is unstructured • NoSQL • Document based – Documents – Texts, tweets – Emails – Machine logs – Blogs – Web pages – Photos – Videos (YouTube) • Graph based – Social media sites – Facebook has 1.1billions users (Microstrategy, July 27, 2013) 10
- 11. Why do we need to use big data? Use in public and private sector to: • Make faster and more accurate business decisions • Make accurate predictions • Gain competitive advantage • Implement smarter marketing – CRM • Discover new opportunities • Enhance Business Intelligence • Enable fraud detection • Reduce crime • Improve scientific research • Quicken analysis (up to real time) – Weeks, days minutes, seconds 11
- 12. Big Data Startup - Case Study • Rocket Fuel • No. 4 on Forbes' 2013 Most Promising Companies In America list • Digital advertising startup • Screens over 26 billion ads per day • “Advertising that learns” big data platform • Distributed planet-scale computing engine • Hadoop implementation • Founders from Yahoo!, Salesforce.com, DoubleClick • Targeting algorithms use lifestyle, purchase intent and social data 12
- 14. 3. Databases 14
- 16. Relational databases – SQL Proprietary • Oracle DB • IBM DB2 • Microsoft SQL • SAP • EMC Open Source • MySQL • PostgresQL • Drizzle • Firebird 16
- 17. Non-relational databases – NoSQL • BigTable – Google • Cassandra – Facebook • Eucalyptus – Amazon • Hbase – Hadoop • MongoDB – 10Gen • Neo4j - NeoTechnologies • CouchDB - Apache • CouchBase • Riak - Basho • Redis - Pivotal 17
- 18. 4. Big Data Analytics 18
- 19. Big Data Analytics - Incumbents • Oracle – Exadata, Exalytics • Microsoft – HDInsight, xVelocity • IBM – Netezza, Cognos, BigInsights • SAP – HANA, Business Objects • EMC – Pivotal (Greenplum) • HP – Vertica, HAVEn • All run on Hadoop 19
- 20. Big Data Analytics – Pure Plays • Pure plays – definition: – Been around more than 20 years – Purely data analytic companies • Teradata - Aster • SAS • Microstrategy 20
- 21. Big Data Analytics – New Entrants • Hortonworks • Cloudera • MapR • Acunu • Pentaho • Tableau • Talend • Splunk 21
- 22. (Some of) IBM’s Big Data Acquisitions • Algorithmics – Oct 2011, $400million • OpenPages – Oct 2010, ? • Netezza – Sept 2010, $1.7billion • SPSS – Jan 2010, $1.2billion • Cognos – Jan 2008, $4.9billion • About $10billion in four years http://en.wikipedia.org/wiki/List_of_mergers_and_acquisitions_by_IBM 22
- 23. Big Data Science Tools • Hadoop • NoSQL • MapReduce • R • Matlab • Python • Statistics 23
- 24. Big Data Hadoop Stack • Hadoop is the de facto big data operating system • Developed from Google and Yahoo! (2005) • It is distributed, open source and managed by Apache 24
- 25. Analytic Technologies • A/B testing • Genetic algorithms • Machine learning • Natural language processing • Neural networks • Pattern recognition • Anomaly detection • Decision tree • Predictive modeling • Regression testing • Sentiment analysis • Signal processing • Simulations • Time series analysis • Visualization • Multivariate analysis • Text analytics 25
- 26. Glossary • OLTP = On Line Transactional Processing • OLAP = On Line Analytic Processing • ODBC = Open DataBase Connectivity • IMDB = In Memory DataBase • CRUD = Create, Read, Update, Delete • ETL = Extract, Transform and Load • CDO = Chief Data Officer • NLP = Natural Language Processing • GQL = Graph Query Language • AaaS = Analytics as a Service • EDW = Enterprise Data Warehouse 26
- 27. References • Microstrategy website, 27 July, 2013, Michael Saylor Presentation at Microstrategy World 2013, http://www.microstrategy.com/ • Teradata website www.teradata.com • Wikipedia http://en.wikipedia.org/wiki/ • Google images www.google.co.uk • IBM website www.ibm.com • Youtube www.youtube.com • Hadoop www.hortonworks.com 27