Vodafone xone fev142013v3 ext
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Big data and security involves managing huge amounts of data from various sources. Some key points: - The amount of data generated annually is expected to grow exponentially to over 6.6 zettabytes by 2016. Individual companies like Facebook generate over 400 terabytes of data per day. - Big data comes from a variety of structured and unstructured sources, and is distributed across multiple locations and systems. Both batch-based and real-time streaming approaches are used. - Effectively organizing, analyzing, and deriving value from large, diverse datasets requires new approaches that can handle different data types and structures from many online and offline sources.
Vodafone xone fev142013v3 ext
- 1. Big Data and Security Michel Burger
- 2. 0.05 ounces/ton Gold mining is about dirt management
- 3. About 11850 Amps to generate around 8.4 Tesla fields (about 150000 times the earth magnetic field) but they operate at low Voltage A lot of what LHC is about is electricity flow management
- 4. How BIG? BIG data is like the LHC combined with gold extraction - Huge amount of data -> 6.6 Zettabytes/year by 2016 (Cisco Cloud Index) - Big flow of data -> 400TB/day (Facebook) - LHC generates 10-15 Petabytes/year of data for each experiment
- 5. The essence of new service providers BI Based Revenue Models (eg Advertisement) User Core Semantic Improves Consumes experience Data Set Mindmap Revenue from Value enriched Data existing services generates revenue Data Service will shrink Service Produces Service Additional revenue from new services The more context the more efficient and One data set Many free services the more value and common semantic Example: Search/Information Management : Rated auction/Selling:
- 6. Classic Approach • Structured Data • Data in the range of Gigabytes to Terabytes • Centralized (Data is imported in analytics) • Batch based • Data silos ETL ETL ETL Transaction Relational Data Analyse Database Warehouse Where is the data that answer my questions ?
- 7. Big Data Approach • Multi Structured Data • Data in the range of Terabytes to Petabytes • Distributed/Federated (Analytics grab the data) • Streaming based • Holistic Data Clusters 1 Stream 2 Organize Analyse 3 n Here are the questions and the data for the answers
- 8. A new pattern • Many different data structures • Many different ways to extract the data Knowledge • Structured • Many different locations (even for the References API Services Content Sources Applications Social Networks Buffering same type of data) • Proprietary • RAN Graph • Batch and Realtime based Data card • Data as a Service Neural Network • Buffered or stream Sim Card Premise Network Core • Connected Things Connected (Consumer, Enterprise) Gateway Relational • Correlation parameters • Unstructured Devices IT Infrastructure Consumption Buffering Report Statistics • Streaming • Taping at Source Real-time Cheap Storage High Efficient Storage Low level Semantic • Buffering, Routing, Filtering • Taping on Stream • Structured/Unstructured • Consumption to Stream Graph Network/ store Source Analysis • Event Collector • Batch Process/Multi Non Real-time Rich Semantic Structure Stream • Multi Stage Store/Process Neura l Network/ Analysis
- 9. With added security Knowledge References API Services Content Sources Social Networks Applications • Strong access RAN control based Data as a Service Data card Sim Card Connected Things (Consumer, Enterprise) Premise Gateway Network Core on industry Connected Devices IT Infrastructure standard Consumption (user, dev, app lication) Report Statistics • Securing the infrastructure (public, private) • Strong • Policy (internal/external) authorization • On-going assessment (DDOS, Penetration …) control based • Data leakage • on open Stream Migration Graph standard Network/ • Securing the identity Analysis • Validating ID • Analytics • Anonymization applied to • Securing the access Analytics • Distributed permission/preference • 3rd party permission Neura l Network/ Analysis
- 10. Final thoughts 1. We need to eliminate the silos – Sources or Usage 2. Still very much a collection of technologies – The assembly is still very complex 3. Is everything about events? 4. We need to handle the CAP theorem more appropriately 5. What is the user experience (not just the end user but also the admin)
- 11. Thank You