Vodafone xone fev142013v3 ext

Big Data and Security

Michel Burger

Gold mining is about dirt management

0.05 ounces/ton

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

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

The essence of new service providers

Service

Data

Consumes

Produces

Improvesexperience

Many free servicesOne data setand common semantic

The more context the more efficient and

the more value

Value enriched Datagenerates

revenue

Example: Search/Information

Management :

Rated auction/Selling:

User

BI Based Revenue Models

(eg Advertisement)

Core Semantic

Data SetMindmap

ServiceServic

e

Revenue from existing services will shrink

Additionalrevenue from new services

Classic Approach• Structured Data• Data in the range of Gigabytes to Terabytes• Centralized (Data is imported in analytics)• Batch based• Data silos

Transaction RelationalDatabase

DataWarehous

e

Analyse

Where is the data that answer my questions ?

ETL ETL ETL

• Multi Structured Data• Data in the range of Terabytes to Petabytes• Distributed/Federated (Analytics grab the data)• Streaming based• Holistic Data

Big Data Approach

2

3

n

1

Organize AnalyseStream

Clusters

Here are the questions and the data for the answers

A new patternS

ou

rces

Str

eam

Con

su

mp

tion

API

Data cardSim Card

Content Services

KnowledgeReferences

Applications

Social Networks

Connected Things (Consumer, Enterprise)Connected

Devices

Network Core

IT Infrastructure

RAN

Premise Gateway

• Many different data structures• Many different ways to extract the

data• Many different locations (even for

the same type of data)• Batch and Realtime based• Buffered or stream• Correlation parameters

• Buffering, Routing, Filtering

• Structured/Unstructured store

• Event Collector• Batch Process/Multi

Structure Stream • Multi Stage

Store/Process

Non

Real-

tim

eR

eal-

tim

e

Low

level S

em

an

tic

Ric

h S

em

an

tic

Ch

eap

Sto

rag

eHig

h E

fficie

nt

Sto

rag

e

Report Statistics

Data as a Service

Graph Network/Analysis

Neura l Network/Analysis

• Structured Buffering• Proprietary• Graph• Neural Network• Relational

• Unstructured Buffering

• Streaming• Taping at

Source• Taping on

Stream • Consumption

to Source

With added securityS

ou

rces

Str

eam

Con

su

mp

tion

API

Data cardSim Card

Content Services

KnowledgeReferences

Applications

Social Networks

Connected Things (Consumer, Enterprise)Connected

Devices

Network Core

IT Infrastructure

RAN

Premise Gateway

Report Statistics

Data as a Service

Graph Network/Analysis

Neura l Network/Analysis

• Securing the infrastructure (public, private)• Policy (internal/external)• On-going assessment (DDOS, Penetration …)• Data leakage• Migration

• Securing the identity• Validating ID• Anonymization

• Securing the access• Distributed permission/preference• 3rd party permission

• Strong access control based on industry standard (user, dev, application)

• Strong authorization control based on open standard

• Analytics applied to Analytics

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

appropriately5. What is the user experience (not just the end user

but also the admin)

Thank You