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Big data session five ( a )f
- 1. 1 Big Data Towards a Secure Digital Future Zimbabwe , June 2015 [email protected] This report is solely for the use at CSZ presentation. No part of it may be circulated, quoted, or reproduced for distribution outside the client organization without prior written approval from MorniPac Consultants This material was used by MorniPac Consultants during an oral presentation; it is not a complete record of the discussion. Computer Society of Zimbabwe Business School
- 2. IBM big data • Oracle big data • Microsoft big dataIBMbigdata•Oraclebigdata IBMbigdata•Oraclebigdata THINK IBM big data • Oracle big data • Microsoft big data
- 3. Basic Computer Principals 3 Network Principals, Microwave, Optic Fibre, Diginet leased line life cycles, Telkom Operations, Neotel Operations, Broadlink Operations, GSM, MTN, Vodacom, Metro Fibre Networks Operations, Metro Connect Operations, ADSL, 3G, POP, POIL, Gateways, Capacity Planning, Bandwidth Scoping, Server Room Planning, PBX, CCTV, MPLS, Basic Cisco Router Configurations, ITIL, Infrastucture, Planning, Administration, Mega Line, Wireless, ATM, SLA, MPLS, Prosesses, Procedures, Training, Voip, Audit, Telco, Vsat. Service Providers worked with. Telkom, Neotel, IS, MTN, Vodacom, CSI, Atlantic, Amvia, Metro Connect, Metro Fibre Networks, Dark Fibre Africa, VTSC, Datapro, Verizon, MTNNS, Broadlink, Logical Wireless, Comsol, Storm
- 4. Theme Large-Scale Data Management Big Data Analytics Data Science and Analytics How to manage very large amounts of data and extract value and knowledge from them keeping it safe in the digital space. 4
- 5. Introduction to Big Data What is Big Data? What makes data, “Big” Data? 5
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- 7. Big Data Definition • There is No single standard definition… “Big Data” …is data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. 7
- 8. Business-Centric Big Data Enables You to Start With a Critical Business Pain and Expand the Foundation for Future Requirements “Big data” isn’t just a technology—it’s a business strategy for capitalizing on information resources Getting started is crucial Success at each entry point is accelerated by products within the Big Data platform Build the foundation for future requirements by expanding further into the big data platform
- 9. Some Characteristics of Big Data: Scale (Volume) • Data Volume • 44x increase from 2009 2020 • From 0.8 zettabytes to 35zb • Data volume is increasing exponentially 9 Exponential increase in collected/generated data
- 10. Characteristics of Big Data: Complexity (Varity) • Various formats, types, and structures • Text, numerical, images, audio, video, sequences, time series, social media data, multi-dim arrays, etc… • Static data vs. streaming data • A single application can be generating/collecting many types of data 10
- 11. Characteristics of Big Data: Speed (Velocity) • Data is 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 11
- 13. Some Talk of 4V’s 13
- 14. Harnessing Big Data • OLTP: Online Transaction Processing (DBMSs) • OLAP: Online Analytical Processing (Data Warehousing) • RTAP: Real-Time Analytics Processing (Big Data Architecture & technology) 14
- 15. Who Generates Big Data Social media and 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 15
- 16. The Model Has Changed… • 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 16
- 17. What’s driving Big Data - 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 17
- 18. Value of 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 18
- 19. Challenges in Handling Big Data • The Bottleneck is in technology • New architecture, algorithms, techniques are needed • Also in technical skills • Experts in using the new technology and dealing with big data 19
- 20. What Technology Do We Have For Big Data ?? 20
- 21. Big Data is a Hot Topic Because Technology Makes it Possible to Analyze ALL Available Data Cost effectively manage and analyze all available data in its native form unstructured, structured, streaming ERP CRM RFID Website Network Switches Social Media Billing
- 22. BIG DATA is not just HADOOP Manage & store huge volume of any data Hadoop File System MapReduce Manage streaming data Stream Computing Analyze unstructured data Text Analytics Engine Data WarehousingStructure and control data Integrate and govern all data sources Integration, Data Quality, Security, Lifecycle Management, MDM Understand and navigate federated big data sources Federated Discovery and Navigation
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- 25. BigInsights Data warehouse Traditional analytic tools Big Data analytic applications Filter Transform Aggregate BigInsights and the data warehouse
- 26. 3 – Simplify your warehouse Customer need – SIGNIFICANTLY • Make performance of DWH better • Reduce DWH administration costs Value statement • Speed: 10 – 100x better performance • Simplicity: Administration costs reduced by 75% - 90% • Scalability • Smart system • In-database analytics • Out-of-the box integration with SPSS Solution • IBM Netezza renamed to • PureData System for Analytics