big data and its implications - a-eskwadraat

Cisco Confidential 1 © 2010 Cisco and/or its affiliates. All rights reserved.

Big Data and Its Implications

Sen Chandaka and Jing Wang

Cisco, The Netherlands

2 © 2012 Cisco and/or its affiliates. All rights reserved.

“data sets whose size is beyond the ability of commonly

used software tools to capture, manage, and process the

data within a tolerable elapsed time.” – Prof. Lev

Manovich, University of California-San Diego

"Big Data are high-volume, high-velocity, and/or high-

variety information assets that require new forms of

processing to enable enhanced decision making, insight

discovery and process optimization.” - Gartner

1 Petabyte = 20,000,000

4 © 2012 Cisco and/or its affiliates. All rights reserved. From Flickr [some rights reserved by Akakumo]

http://www.flickr.com/


Internet and Social media Business Transaction

Internet of Things / M2M Multimedia Content


50 Billion Connected Devices

1 Million Applications

1 Zettabyte of Content

6 Million Security Threats


Big Data – Overview

What is Big Data

Importance of Big data

The Hadoop Framework

Vendors in Big Data

Big Data Efforts at Cisco

Data volume is growing 40% per year and will increase 44 times between 2010 and 2020 [McKinsey Global Institute]


V o l u m e

V e l o c i t y

V a r i e t y

V a l u e


Source: US Bureau of Labor Statistics; McKinsey Global Institute analysis


Integrated Data Warehouse ~80TB in Prod. Dual Active environment with CoD and online archive environment

Analytics Tier: DM’ss and environments

housing relevant data extracts, potential tiered

DW archive space

Common Logging, source data unload platform, and

repository for other unstructured and semi-structured data sources

Application Data Size

HANA MR

Server Vendors

Commodity

Server

Vendors

1TB 10TB 100TB 10PB

Co

mp

eti

tio

n

Server Vendors, Appliance Vendors:

Oracle Exadata, Teradata, Neteeza

Greenplum DB (DCA), Oracle Big Data

Appliance, HP Vertica

Web 2.0

VBlock

Open Source

High

performance BI

Appliance

VectorWise

In-Memory DW

Massive

Scale-out

<5TB

10-40TB

50-500TB

100TB-5PB Database

Cisco Switching

Server Vendors

http://hadoop.apache.org/

http://paraccel.com/


14

Data Warehouse Modernization

• Over 4X more performance

• 50% more cores, 3X more threads

• New virtualization & security features

• Lower power

• *Third party brands and names may be claimed by others

• Source: Publicly posted scores on http://www.tpc.org/tpch/default.asp as of 5-May-2011. See backup for system configurations , availability & QphH

Software & workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations & functions. Any

change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more

information go to http://www.intel.com/performance. See “Configurations” page in backup for configuration details.

2-Processors (Xeon® 5355)

HP ProLiant* ML370G5

2-Processors (Xeon® 5680)

HP ProLiant DL380G7

4.18

1.00

2007 Server 2011 Server

Data

Ware

house P

erf

orm

ance

TP

C-H

, Q

phH

, 100 G

B S

calin

g

(Hig

her

score

s a

re b

etter)

17,687 73,975

4-Processors (Xeon® E7-8837)

Dell PowerEdge* R910 w/ VectorWise* 1.6

2011 Server

Data

Ware

house P

erf

orm

ance

TP

C-H

, Q

phH

, 300 G

B S

calin

g

(Hig

her

score

s a

re b

etter)

400, 932

Data Warehouse Scale Up (From 2 to 4 Processors) • 4-processor servers support up to 2TB of

memory for top performance on larger data warehouses

http://www.tpc.org/tpch/default.asp

http://www.intel.com/performance


• Open-source software framework that supports data-intensive distributed applications

• Apache project written in Java programming language

• Derived from Google’s MapReduce and Google File System (GFS)

• Includes a number of projects – MapReduce, Hbase, HDFS, Pig, Hive, etc


Database

NoSQL Database

\

Exclusive hardware reference

Joint GTM

Several joint engagements

UCS is exclusive hardware reference

Several joint engagements UCS is the only partner platform

Commercial, distributed key-value

database.

Commercial

Document-oriented

database

NetApp Open Solution for Hadoop (NOSH)

http://paraccel.com/

http://en.wikipedia.org/wiki/File:Marklogic-logo.PNG

© 2010 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 18

Private Cloud Automation

Research/ Academia

Experimental

OpenFlow/SDN

components for

production

networks

Massively Scalable Data Center

Customize with

Programmatic

APIs to provide

deep insight into

network traffic

Service Providers

Policy-based

control and

analytics to

optimize and

monetize

service delivery

Enterprise

Virtual

workloads, VDI,

Orchestration of

security profiles

Cloud

Automated

provisioning

and

programmable

overlay,

OpenStack

Diverse Programmability Requirements Across Segments (Automation & Programmability)

Scalable Multi-Tenancy

Network Flow Management

Network “Slicing”

Agile Service Delivery


Technical

Advisory Group

Chair,

Working Groups:

Config, Hybrid,

Extensibility,

Futures/FPMOD/

OF2.0

802.1 Overlay Networking

Projects, Cisco

Innovations:

FEX Architecture

Overlay Working Groups:

NVO3, L2VPN, TRILL, L3VPN, LISP,

PWE3

API Working Groups:

NETCONF, ALTO, CDNI, XMPP, SDNP,

I2AEX

Controller Working Groups:

PCE, FORCES

Open Source

Cloud

Computing

project

Open Network

Research Center at

Stanford University

Working Groups:

Quantum API

Donabe

Cisco Innovations:

OpenStack API for

Nexus

OpenStack Extensions

Note: Very little standardization in hypervisor technologies (e.g. live migration, config, APIs)


Resilient. Scalable. Secure.

Rich-features. Evolutionary

Investment Protection

Simpler. Fewer nodes to manage.

Topology View Combined Benefits

Control Plane

Data Plane

Control Plane

Data Plane

Control Plane

Data Plane

Control Plane

Current Model “SDN approach” Hybrid Model?


- “Big data: The next frontier for innovation, competition, and

productivity”, McKinsey Global Institute

- Google Flu Trends project

- Urban EcoMap

- Hadoop World NY

- Big Data in the Enterprise

- How Companies Learn Your Secrets

http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation





http://www.google.org/flutrends/

http://urbanecomap.org/

http://strataconf.com/stratany2012/?_discount=CLOUDERA&cmp=ex-strata-stny12-cloudera-discount



http://www.cisco.com/en/US/prod/collateral/switches/ps9441/ps9670/white_paper_c11-690561.html

http://www.cisco.com/en/US/prod/collateral/switches/ps9441/ps9670/white_paper_c11-690561.html

http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=1&




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Sales Training Phase 1

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Business, sales,

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curriculum delivered

virtually

in state-of-the-art

classrooms that

leverage Cisco

TelePresence,

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iPad technologies.

Virtual

Learning

(Months 1-3)

Phase 1

(Q1-Q2 FY12)

Associates move

into a sales or

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where they will get

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interacting with

customers and

partners and will be

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and engineering

professionals.

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Experience

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After successfully

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Associates are

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Manager or Virtual

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Join the

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(Month 13)


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(ASR + ASE)

London (ASR + ASE)

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Mexico City

(ASE)

Sao Paulo

(ASR+ASE)

Moscow (ASR)

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Training Hub Location

Mexico ASEs to train in RTP Year 1 and will return to home country in Year 2 (based on business need)

Johannesbu

rg

(ASR)

Dubai (ASR)

Moscow, Saudi Arabia, UAE and South Africa ASRs train in Amsterdam Year 1 and will return to home country in Year 2 (based

on business need)


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Thank you.

big data and its implications - a-eskwadraat

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