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Lecture1 BIG DATA and Types of data in details
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- 3. Topics • Scope: BigData & Analytics • Topics: – Foundation of Data Analytics and Data Mining – Hadoop/Map-Reduce Programming and Data Processing & BigTable/Hbase/Cassandra – Graph Database and Graph Analytics 3
- 4. What’s Big Data? Nosingle definition; here is from Wikipedia: • Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. • The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. • The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.” 4
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- 6. Volume (Scale) • DataVolume – 44x increase from 2009 2020 – From 0.8 zettabytes to 35zb • Data volume is increasing exponentially 6 Exponential increase in collected/generated data
- 7. 12+ TBs of tweetdata every day 25+ TBs of log data every day ? TBs of data every day 2+ billion people on the Web by end 2011 30 billion RFID tags today (1.3B in 2005) 4.6 billion camera phones world wide 100s of millions of GPS enabled devices sold annually 76 million smart meters in 2009… 200M by 2014
- 8. Maximilien Brice, ©CERN CERN’s Large Hydron Collider (LHC) generates 15 PB a year
- 9. The Earthscope • TheEarthscope is the world's largest science project. Designed to track North America's geological evolution, this observatory records data over 3.8 million square miles, amassing 67 terabytes of data. It analyzes seismic slips in the San Andreas fault, sure, but also the plume of magma underneath Yellowstone and much, much more. (http://www.msnbc.msn.com/id/4436 3598/ns/technology_and_science- future_of_technology/#.TmetOdQ--uI)
- 10. Variety (Complexity) • RelationalData (Tables/Transaction/Legacy Data) • Text Data (Web) • Semi-structured Data (XML) • Graph Data – Social Network, Semantic Web (RDF), … • Streaming Data – You can only scan the data once • A single application can be generating/collecting many types of data • Big Public Data (online, weather, finance, etc) 10 To extract knowledge all these types of data need to linked together
- 11. A Single Viewto the Customer Customer Social Media Gaming Entertain Banking Finance Our Known History Purchas e
- 12. Velocity (Speed) • Datais begin generated fast and need to be processed fast • Online Data Analytics • Late decisions missing opportunities • Examples – E-Promotions: Based on your current location, your purchase history, what you like send promotions right now for store next to you – Healthcare monitoring: sensors monitoring your activities and body any abnormal measurements require immediate reaction 12
- 13. Real-time/Fast Data Social mediaand networks (all of us are generating data) Scientific instruments (collecting all sorts of data) Mobile devices (tracking all objects all the time) Sensor technology and networks (measuring all kinds of data) • The progress and innovation is no longer hindered by the ability to collect data • But, by the ability to manage, analyze, summarize, visualize, and discover knowledge from the collected data in a timely manner and in a scalable fashion 13
- 14. Real-Time Analytics/Decision Requirement Customer Influence Behavior Product Recommendations thatare Relevant & Compelling Friend Invitations to join a Game or Activity that expands business Preventing Fraud as it is Occurring & preventing more proactively Learning why Customers Switch to competitors and their offers; in time to Counter Improving the Marketing Effectiveness of a Promotion while it is still in Play
- 15. Some Make it4V’s 15
- 16. Harnessing Big Data •OLTP: Online Transaction Processing (DBMSs) • OLAP: Online Analytical Processing (Data Warehousing) • RTAP: Real-Time Analytics Processing (Big Data Architecture & technology) 16
- 17. The Model HasChanged… • The Model of Generating/Consuming Data has Changed Old Model: Few companies are generating data, all others are consuming data New Model: all of us are generating data, and all of us are consuming data 17
- 18. What’s driving BigData - Ad-hoc querying and reporting - Data mining techniques - Structured data, typical sources - Small to mid-size datasets - Optimizations and predictive analytics - Complex statistical analysis - All types of data, and many sources - Very large datasets - More of a real-time 18
- 19. Big Data: Batch Processing& Distributed Data Store Hadoop/Spark; HBase/Cassandra BI Reporting OLAP & Dataware house Business Objects, SAS, Informatica, Cognos other SQL Reporting Tools Interactive Business Intelligence & In-memory RDBMS QliqView, Tableau, HANA Big Data: Real Time & Single View Graph Databases THE EVOLUTION OF BUSINESS INTELLIGENCE 1990’s 2000’s 2010’s Speed Scale Scale Speed
- 20. Big Data Analytics •Big data is more real-time in nature than traditional DW applications • Traditional DW architectures (e.g. Exadata, Teradata) are not well- suited for big data apps • Shared nothing, massively parallel processing, scale out architectures are well-suited for big data apps 20
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- 23. Cloud Computing • ITresources provided as a service – Compute, storage, databases, queues • Clouds leverage economies of scale of commodity hardware – Cheap storage, high bandwidth networks & multicore processors – Geographically distributed data centers • Offerings from Microsoft, Amazon, Google, …
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- 25. Benefits • Cost &management – Economies of scale, “out-sourced” resource management • Reduced Time to deployment – Ease of assembly, works “out of the box” • Scaling – On demand provisioning, co-locate data and compute • Reliability – Massive, redundant, shared resources • Sustainability – Hardware not owned
- 26. Types of CloudComputing • Public Cloud: Computing infrastructure is hosted at the vendor’s premises. • Private Cloud: Computing architecture is dedicated to the customer and is not shared with other organisations. • Hybrid Cloud: Organisations host some critical, secure applications in private clouds. The not so critical applications are hosted in the public cloud – Cloud bursting: the organisation uses its own infrastructure for normal usage, but cloud is used for peak loads. • Community Cloud
- 27. Classification of CloudComputing based on Service Provided • Infrastructure as a service (IaaS) – Offering hardware related services using the principles of cloud computing. These could include storage services (database or disk storage) or virtual servers. – Amazon EC2, Amazon S3, Rackspace Cloud Servers and Flexiscale. • Platform as a Service (PaaS) – Offering a development platform on the cloud. – Google’s Application Engine, Microsofts Azure, Salesforce.com’s force.com . • Software as a service (SaaS) – Including a complete software offering on the cloud. Users can access a software application hosted by the cloud vendor on pay- per-use basis. This is a well-established sector. – Salesforce.coms’ offering in the online Customer Relationship Management (CRM) space, Googles gmail and Microsofts hotmail, Google docs.
- 28. Infrastructure as aService (IaaS)
- 29. More Refined Categorization •Storage-as-a-service • Database-as-a-service • Information-as-a-service • Process-as-a-service • Application-as-a-service • Platform-as-a-service • Integration-as-a-service • Security-as-a-service • Management/ Governance-as-a-service • Testing-as-a-service • Infrastructure-as-a-service InfoWorld Cloud Computing Deep Dive
- 30. Key Ingredients inCloud Computing • Service-Oriented Architecture (SOA) • Utility Computing (on demand) • Virtualization (P2P Network) • SAAS (Software As A Service) • PAAS (Platform AS A Service) • IAAS (Infrastructure AS A Servie) • Web Services in Cloud
- 31. Enabling Technology: Virtualization Hardware OperatingSystem App App App Traditional Stack Hardware OS App App App Hypervisor OS OS Virtualized Stack
- 32. Everything as aService • Utility computing = Infrastructure as a Service (IaaS) – Why buy machines when you can rent cycles? – Examples: Amazon’s EC2, Rackspace • Platform as a Service (PaaS) – Give me nice API and take care of the maintenance, upgrades, … – Example: Google App Engine • Software as a Service (SaaS) – Just run it for me! – Example: Gmail, Salesforce
- 33. Cloud versus cloud •Amazon Elastic Compute Cloud • Google App Engine • Microsoft Azure • GoGrid • AppNexus
- 34. The Obligatory TimelineSlide (Mike Culver @ AWS) COBOL, Edsel Amazon.com Darkness Web as a Platform Web Services, Resources Eliminated Web Awareness Internet ARPANET Dot-Com Bubble Web 2.0 Web Scale Computing
- 35. AWS • Elastic ComputeCloud – EC2 (IaaS) • Simple Storage Service – S3 (IaaS) • Elastic Block Storage – EBS (IaaS) • SimpleDB (SDB) (PaaS) • Simple Queue Service – SQS (PaaS) • CloudFront (S3 based Content Delivery Network – PaaS) • Consistent AWS Web Services API
- 36. What does Azureplatform offer to developers?
- 37. June 3, 2008Slide 37 Google AppEngine vs. Amazon EC2/S3 Google’s AppEngine vs Amazon’s EC2 AppEngine: • Higher-level functionality (e.g., automatic scaling) • More restrictive (e.g., respond to URL only) • Proprietary lock-in EC2/S3: • Lower-level functionality • More flexible • Coarser billing model VMs Flat File Storage Python BigTable Other API’s
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- 39. Topic 1: DataAnalytics & Data Mining • Exploratory Data Analysis • Linear Classification (Perceptron & Logistic Regression) • Linear Regression • C4.5 Decision Tree • Apriori • K-means Clustering • EM Algorithm • PageRank & HITS • Collaborative Filtering
- 40. Topic 2: Hadoop/MapReduce Programming& Data Processing • Architecture of Hadoop, HDFS, and Yarn • Programming on Hadoop • Basic Data Processing: Sort and Join • Information Retrieval using Hadoop • Data Mining using Hadoop (Kmeans+Histograms) • Machine Learning on Hadoop (EM) • Hive/Pig • HBase and Cassandra
- 41. Topic 3: GraphDatabase and Graph Analytics • Graph Database (http://en.wikipedia.org/wiki/Graph_database) – Native Graph Database (Neo4j) – Pregel/Giraph (Distributed Graph Processing Engine) • Neo4j/Titan/GraphLab/GraphSQL
- 42. Textbooks • No OfficialTextbooks • References: • Hadoop: The Definitive Guide, Tom White, O’Reilly • Hadoop In Action, Chuck Lam, Manning • Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer (www.umiacs.umd.edu/~jimmylin/MapReduce-book- final.pdf) • Data Mining: Concepts and Techniques, Third Edition, by Jiawei Han et al. • Many Online Tutorials and Papers 42
- 43. Cloud Resources • Hadoopon your local machine • Hadoop in a virtual machine on your local machine (Pseudo-Distributed on Ubuntu) • Hadoop in the clouds with Amazon EC2
- 44. Course Prerequisite • Prerequisite: –Java Programming / C++ – Data Structures and Algorithm – Computer Architecture – Basic Statistics and Probability – Database and Data Mining (preferred) 44
- 45. This course isnot for you… • If you do not have a strong Java programming background – This course is not about only programming (on Hadoop). – Focus on “thinking at scale” and algorithm design – Focus on how to manage and process Big Data! • No previous experience necessary in – MapReduce – Parallel and distributed programming
- 46. Grade Scheme 46 Homework 70% Project ClassParticipation 20% 10% • Each Class will have a sign-in sheet • Zero-Tolerance on plagiarism
- 47. Project • Project (dueApril 24th) – One project: Group size <= 4 students – Checkpoints • Proposal: title and goal (due March 1st) • Outline of approach (due March 15th) • Implementation and Demo (April 24th and 26th) • Final Project Report (due April 29th) – Each group will have a short presentation and demo (15-20 minutes) – Each group will provide a five-page document on the project; the responsibility and work of each student shall be described precisely 47