where do we go from here? lessons and landmarks from … · gateway with kafka spark streaming...

Where do we go from here?Lessons and Landmarks from Real-World Cisco UCS Big Data Deployments

Han Yang, Senior Product Manager, Cisco

Raghunath Nambiar, CTO, Cisco

BRKINI-2021

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public

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cs.co/ciscolivebot#BRKINI-2021

“Extracting value from information is not primarily a matter of how much data you have . . ., it’s how much you use them to create new or better approaches to doing business.”

Less than half of structured data is actively used in decision making

Less than 1% of unstructured data is analyzed or used at all

May, 2017

How are you managing

your data as part of

your business strategy?

https://hbr.org/2017/05/whats-your-data-strategy

• What is your data

acquisition strategy

from new product and

services?

• Are you consolidating

your existing data sets

into a data hub?

• Do you have AI center

of excellence to pool

talent?

What is Your Corporate Strategy in the World of AI?

Andrew Ng:

• Stanford Professor

• Led Google Brain

• Drove AI at Baidu

EmTech, Dec, 2017: https://www.youtube.com/watch?v=NKpuX_yzdYs

© 2017 Cisco and/or its affiliates. All rights reserved. Cisco Confidential

7


15,000 206

31 8,000

connected athletes countries

buildings rooms

9 milliontickets sold & validated

85,000soldiers &

policemen

70,000volunteers

Olympic Village with

fro

m

25,000media personnel

42sports

in

Every person, venue,

device was

connected.

It was the most

connected event in

the history


The first truly connected Olympics

5 billion

1 trillion visitors to official Olympic web sites

TV spectators from more than 200

countries. That’s about 66% of world population…

hours of online video contentthat’s about equal to one person watching 20 years of video without a break

hours of video watched via mobile

170,000

130 million

54 million data feeds transported and processed


Education: Connected

classrooms over a stable,

secure, reliable network.

Crisis Management:

Protect your business by

seeing what’s happening in

real time.

Health Care Management:

Electronic Medical Records

to spot trends in chronic

diseases.

the possibilities are endless. …

Be more effective. Make better decisions. Protect and secure.

Operations: Massive scale of technology and products to

plan, design, optimize your data center.

BRKINI-2021 10


Lessons being learned from smart cities

finance, construction, transportations, media, utilities, health


Cities

1990: 10

2015: 29

2030: 41

1990: 270

2015: 488

2030: 662

Mega-Cities >1M Pop.


Today, cities consume 75% of worlds resources and

generates 80% of greenhouse gases

2015

3.9 Billion

2030

5.1 Billion

Urban Population Growth

4.3 Billion

2020

There are several areas of improvement

Make cites cleaner, safer, efficient and sustainableSmart City


Smart Cities: Dream vs. Reality


Areas of

Improvement

Energy

Transportation

Environmental Monitoring

Safety

Emergency Preparedness

Make the City Smart

BRKINI-2021https://hbr.org/2013/01/why-it-fumbles-analytics


Are You the Cause of Analytics Fumble?

Traditional IT Project Big Data Analytics Project

Typical Project

• Install ERP System

• Optimize supply chain performance

Typical Project

• Understand customers’ needs and

behavior

• Predict future growth markets

Overarching Goals

• Improve efficiency

• Lower Cost

Overarching Goals

• Challenge assumptions & bias for

decision

• New insight into customer

Project Structure

• Develop detailed plans to deploy,

manage, and train

Project Structure

• Discovery driven: Hypothesis -> Get

Data -> Experiment

BRKINI-2021 17

Types of Analytics

1. Business Analytics

2. Big Data Analytics

3. IT Operation Analytics

4. IoT Analytics

5. Machine Learning

BRKINI-2021 19© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public

Areas of FocusCisco Validated Designs, Case Studies, Performance Benchmarks

Business

Analytics

Big Data

Analytics

IT Operations

Analytics

IoT

Analytics

AI, ML/DL

Analytics

VSOM

Big Data Analytics


IOT Data

Sources

Web Data Sources

Streaming Data Sources

Gateway with Kafka Spark Streaming

Compute Nodes

HDFS

No SQL

Hbase,

Cassandra

….

Apps

Analytics DB

Se

rvin

g L

aye

r

Speed Layer Batch Layer

Lambda Architecture on UCS


Cisco UCS at all layers, fully validated architectures

with all major players

Kafka

Cisco UCS C240

Real-Time Data Store

UCS C220/C240Data Inject (CoAP/MQTT.XMPP Data Processing

Batch

Real-Time

D

a

t

a

A

n

a

l

y

t

i

c

s

F

o

g

F

o

g

F

o

g

F

o

g

F

o

g

F

o

g

Big Data Store

UCS C240/C3160Serving Layer

Speed Layer Batch Layer

DATA

Aggregator

Cisco UCS C240C800/UCS Mini/UCS C240

ISR 8x9 with 4G LTE and Dual

802.11n a/g/n (WiFi) Radios

Managed by Cisco FogDirector

Cisco UCS Infrastructure for Big Data & Analytics

IT Operations Analytics


IT is accountable for:

Becoming proactive and predictive

Meeting/exceeding SLAs

Reducing expenses

Providing end-to-end operational visibility

MORE

DEVICES

MORE

DATA

MORE

APPSDelivered

Continuously

Multi-vendor, siloed IT

infrastructures create management

complexity

MORE

MOBILITY

Network Compute OS ApplicationsStorage

Need for ITOA

IT silos create obstacles

to consistent application,

infrastructure performance

Lack of visibility puts SLAs in jeopardy

BRKINI-2021 24


Required: Real-time Insights, Comprehensive VisibilityIT operations analytics demands innovation in analytics software and infrastructure

Analytics Software Analytics Infrastructure

• Single software platform integrates across

infrastructure silos, enabling visibility to data anywhere

• Flexibility to identify, analyze new data sources

• Fast time to value

• Comprehensive IT management functionality

to improve IT productivity

+Monitoring Incident

Mgmt

Problem

Mgmt

Capacity

Mgmt

+ +Low TCOConsistent, split-

second

response times

Highly

scalable

+ +

IT needs an analytics platform that can meet their needs today and tomorrow

BRKINI-2021 25


IT Operations Analytics on Cisco UCS with SplunkSplunk provides powerful analytics software platform for IT operations

End-to-end operations visibility

Enhances productivity

Fast time-to-value

Full continuum of IT management

Smartphones and Devices

Online Services

PublicCloud

Security

Telecoms

NetworksWeb

Services

Web Clickstreams

RFID

Call Detail Records

On-Premises

GPS Location

Databases

Packaged Applications

Energy Meters

Storage

PrivateCloud

Servers

Messaging

Desktops

OnlineShopping Cart

MonitoringProblem

Management

Capacity

Management

Incident

Management

Index Untapped Data:

Any Source, Type, Volume

+ + +

BRKINI-2021 26

IoT Analytics

• Internet Of Things Market To Reach $267B

• 50% of IoT spending will be driven by manufacturing,

transportation, logistics, and transportation

• IoT Analytics is predicted to generate $21.4B

https://www.forbes.com/sites/louiscolumbus/2017/01/29/internet-of-things-market-to-reach-267b-by-2020/

By 2020

Yet in the IoT world, smooth sailing is rare

26%

of all surveyed companies

are successful with their

IoT Initiatives

Only…


Remote Data

• IoT data can be very

remote

• Costly to send all data

back to data center

• Does all data need to be

sent back to data

center?


Discovery

• Data discovery is an

essential part of data

scientist job

• How do you know what

type of analytics and

models will result?


Investment

• IoT project requires

investment in sensors,

network, analytics . . .

• How to make the

investment when

discovery can lead to

different type of data

collection and analytics

requirements?


Challenges and Opportunities for IoT Data Lifecycle

Remote Data

Raise

Abstraction

Discovery

Edge

Analytics

Investment

Multiple

Options


Data Mgmt. & Pattern Detection

using ESP

Data: power

frequency,

voltage, current,

phasor angle, …

Cisco UCS

Pattern Detection

using ESP

Visualization of

streaming data using

SAS ESP

Streamviewer

Application Life

Cycle Management

(Cisco FogDirector)

Update

Streaming

models

Connect to

external apps

with ESP

connectorsCisco UCS

Cisco UCS

Pattern discovery using SAS

Visual Analytics and SAS Visual

Statistics

Cisco

ISA

KAFKA

Edge Enterprise

Visualization of streaming data using

SAS ESP Streamviewer

Edge to Enterprise IoT Analytics



using ESP

Data: power

frequency,

voltage, current,

phasor angle, …

Cisco UCS

Pattern Detection

using ESP

Visualization of

streaming data using

SAS ESP

Streamviewer

Application Life

Cycle Management

(Cisco FogDirector)

Update

Streaming

models

Connect to

external apps

with ESP

connectorsCisco UCS

Cisco UCS



Statistics

Cisco

ISA

KAFKA

Substation Operations Center

Visualization of streaming data using

SAS ESP Streamviewer

Edge to Enterprise IoT Analytics: Electric Utility


Local and Global Analytics

Global AnalyticsLocal Analytics

Frequency


IR 8x9

• Ruggedized Integrated Services Router

• Runs SAS analytics as data is being gathered

• Securely send relevant summary back to data center

Integrated Infrastructure for Big Data

• Industry leading Cisco UCS

• Tested and validated configuration

• High performance & availability

Cisco Infrastructure

IR 829IR 809



using ESP

Data: power frequency,

voltage, current, phasor

angle, …

Cisco UCS

Pattern Detection

using ESP

Visualization of

streaming data

using SAS ESP

Streamviewer

Application Life Cycle Management (Cisco FogDirector)

Update Streaming

models

Connect to external apps

with ESP connectorsCisco UCS

Cisco UCS



Statistics

Cisco ISA

KAFKA

Visualization of streaming data using SAS ESP Streamviewer

Remote Data and Raised Abstraction




using ESP



angle, …

Cisco UCS

Pattern Detection

using ESP

Visualization of

streaming data

using SAS ESP

Streamviewer


Update Streaming

models



Cisco UCS



Statistics

Cisco ISA

KAFKA


Remote Data and Raised Abstraction

Part of Data Pipeline

Reduce Bandwidth

Raise Abstraction




using ESP



angle, …

Cisco UCS

Pattern Detection

using ESP

Visualization of

streaming data

using SAS ESP

Streamviewer




Cisco UCS



Statistics

Cisco ISA

KAFKA



Discovery and Edge Analytics

Update Streaming

models



using ESP



angle, …

Cisco UCS

Pattern Detection

using ESP

Visualization of

streaming data

using SAS ESP

Streamviewer




Cisco UCS



Statistics

Cisco ISA

KAFKA



Discovery and Edge Analytics

Update Streaming

models

Discovery -> Updated Edge Analytics Model

LoRa ™ Network Features

Low Cost Minimal infrastructure

Low cost end-node

Open SW

Long Range Greater than cellular

Deep indoor coverage

Star topology

Max Lifetime Low power optimized

10-20yr lifetime

>10x vs cellular M2M

Multi-Usage High capacity

Multi-tenant

Public network

Semtech Proprietary and Confidential 12

Field Area Network (Wi-SUN)

AMI smart meteringDistribution automationStreet lightingO&G wellhead monitoringWater/wastewater

Low Power Long Range Wireless (LPWA – LoRA)

SP IoT InfrastructuresBattery powered sensorsEnvironmental monitoringSmart Cities, parking, and AgricultureSP cell tower monitoring

Remote Asset Monitoring

Pipeline monitoringRoadside infrastructureDistribution automationATMsDigital Signage

Fleet VehiclesMass Transit

Automated Vehicle Location tracking, Data Uploaded in Seconds with 4G/LTEHandles Multiple Wireless Laptops, Smartphones, Tablets Simultaneously

Premium Mobile Broadband (PMB)

Public safety and security CPE

CGR1000

SDKIR500

IR910

IR8x9 +

LoRA

Modem

(future)

819H IR829

IR809

IOT Field Network Director/Industrial Operations Kit – Zero Touch Provisioning, Firmware upgrade,…

IOT Software Platform – Fog Computing, BYOI,…

819H

IR829

IR809

819H

Investment with Multiple Options

CGR

M

M

M

M

M

M6

M

M

M

PLC

RF

RF

RF

M

M

M M

M

M

M

M

M M

M

M M M M

M7 M3

M2M8

M5

M4

RF

RFMM

M

MM

M

M

M

M

M

M

M

PLC

PLC

RF

RF

M

M

M

M

M

M

M

M

M

M1


• Expands computational capability of Connected Grid Router

• Specifications:

• 4-core AMD GX410 x86 800 MHz

• 4 GB DDR Memory

• Storage:

• 64GB (50GB available for apps)

• 128GB (100GB available for apps)

• Gigabit Ethernet and two USB interfaces

• -40C to +65C\

CGR 1000 Compute Module

CGR 1000

CGR Compute Module


Cisco UCS Integrated Infrastructure for Big Data and AnalyticsFifth Generation of Reference Architectures and Bundles

UCS B200 M5 UCS-SP-C220M5-A2 UCS-SP-C240M5-A2 UCS-SPC240M5L-S1 UCSS-SP-S3260-BV

• Network: 2x 6332• Servers: 8 X UCS B200 M5 Blades• Server Type: B200 M5 Blade servers• CPU: 2x Intel Xeon Processor Scalable

Family 6132 (2x14 cores , 2.6 GHz)• Memory: 192GB 2666 MHz• Boot: M.2 with 2x480GB SSDs• Storage: 2 x Cisco 2.5-inch 7.7-TB

HGST SN200 NVMe• VIC: VIC 1340• Storage Controller: Cisco FlexStorage

PCIe SSD pass-through module with HDD cage

• Network: 2x 6332• Servers: 8 X UCS-SP-C220M5-A2• Server Type: C220 M5 SFF• CPU: 2x Intel Xeon Processor Scalable

Family 6132 (2x14 cores , 2.6 GHz)• Memory: 192 GB 2666 MHz• Boot: M.2 with 2x480GB SSDs• Storage: 10 x 1.6TB EV SATA SSDs SFF• VIC: VIC 1387• Storage Controller: Cisco 12 Gbps SAS

Modular RAID Controller with 2GB FBWC or Cisco 12 Gbps Modular SAS HBA

• Network: 2x 6332• Servers: 16 X UCS-SP-C240M5-A2• Server Type: C240 M5 SFF• CPU: 2x Intel Xeon Processor Scalable

Family 6132 (2x14 cores , 2.6 GHz)• Memory: 192 GB 2666 MHz• Boot: M.2 with 2x480GB SSDs• Storage: 26 x 1.8TB 10K rpm SAS HDD

SFF or 12 x 1.6TB EV SATA SSDs SFF• VIC: VIC 1387• Storage Controller: Cisco 12 Gbps SAS


• Network: 2x 6332• Servers: 16 X UCS-SPCC240M5L-S1• Server Type: C240 M5 LFF• CPU: 2x Intel Xeon Processor Scalable

Family 4110 (2x8 cores , 2.1 GHz)• Memory: 192 GB 2666 MHz• Boot: M.2 with 2x480GB SSDs• Storage: 12 x 8TB 7.2K rpm SAS HDD

LFF plus 2 x 1.6TB EV SATA SSDs SFF• VIC: VIC 1387• Storage Controller: Cisco 12 Gbps SAS


• Network: 2x 6332• Servers: 8 X Cisco UCS S3260 Storage server • Server Type: S3260 (2 x servers)Each server node with:o CPU: 2 Intel Xeon processor E5-2680 v4

CPUs (2 x 14 cores, 2.4 GHz) o Memory: 256GB 2400 MHzo Boot: 2 x 480GB SSDso Storage: 24 x 6TB 7.2K rpm SAS HDD LFFo VIC: VIC 1387o Storage Controller: Cisco 12 Gbps SAS

Modular RAID Controller with 4GB FBWC


IoT Analytics

• IoT has many challenges

& opportunities

• Remote Data: Raise

Abstraction

• Discovery: Edge Analytics

• Investment: Multiple

Options

Artificial Intelligence / Machine Learning / Deep Learning


What is Artificial Intelligence, Machine Learning, Deep Learning?


Artificial Intelligence, Machine Learning, and Deep Learning

F(x)

Deep

Learning

Artificial

Intelligence

Machine

Learning

Artificial IntelligenceTechnique where computer can

mimic human behavior

Machine LearningSubset of AI techniques which use

algorithms to enable machines to learn from

data

Deep LearningSubset of ML techniques which uses multi-

layer neural network to learn


Examples of Artificial Intelligence, Machine Learning, and Deep Learning

F(x)

Deep

Learning

Artificial

Intelligence

Machine

Learning

Artificial IntelligencePlay Chess: Find all moves and

prune off bad moves

Machine LearningBased on size and price of neighboring

houses, predict price of new house on

market

Deep LearningGiven lots of images of cats and dogs, is

there a cat or dog in a new photo?


5 Levels of Artificial Intelligence

Level Status

Generality Solved

Learning without being Taught

Solved

Transfer Learning Not Yet

Common Sense Not Yet

Self Awareness Still Mystery

https://www.huffingtonpost.com/entry/human-level-ai-how-far-are-

we_us_59ecc013e4b092f9f241931e

https://www.huffingtonpost.com/entry/human-level-ai-how-far-are-we_us_59ecc013e4b092f9f241931e


ComputerData

ProgramOutput

ComputerData

ProgramOutput

Traditional Programming

Supervised Machine Learning


Magic?

No, more like gardening

• Seeds = ML Algorithms

• Nutrients = Data

• Gardener = Data Scientist

• Plants = Programs


ComputerTraining Data

ProgramOutput

Supervised Machine Learning

ComputerNew Data

New Output

Machine Learning Inferencing

Program

Training


• Linear regression is the “go-to” analytic techniques

• Very easy to explain the result, especially in regulated industry like finance

• May not model nonlinear data very well

Machine Learning Algorithm: Linear Regression

Is there a relation between

these 2 variables?

https://hbr.org/2015/11/a-refresher-on-regression-analysis

https://hbr.org/2015/11/a-refresher-on-regression-analysis


Puppy or Muffin?

Deep Learning: Breakthrough in Image Recognition

P


Deep Learning = Neural Networks

Each Edge has

Unique Weight

Training Algorithm Computes the Weight of Each Edge


Deep Learning is Matrix Math Intensive

• Gradient Descent Error Function and its Partial Derivatives

Gradient Descent Error Equation

and Partial Derivatives


How Are Customers Deploying ML?


Apache SparkML

• Apache Spark: Leading

open source analytic

software

• SparkML: Machine

learning packages

integrated with Apache

Spark

• Part of big data Hadoop

software stack

• Uses x86 CPUs

Machine

Learning


Deep Learning Frameworks

• Leading Frameworks

• TensorFlow, PyTorch,

Caffe, Theano

• Not part of Hadoop

distributions

• Run on CPUs or GPUsF(x)

Deep

Learning


Big Data is a Prerequisite for DL

“Data is to AI

what food is to

humans”Berry Smyth, Professor of

Computer Science at University

College Dublin

F(x)

Deep

Learning


New Data Continues to Come In: Retrain


Enterprise Software with ML Features

• SAP Leonardo Machne

Learning

• Part of SAP Cloud Platform

(PaaS that can run on-

premise too)

• Create applications with ML

capabilities

• SAS Viya

• Companion to SAS

Foundation

• Includes ML algorithms to

complement statistic

techniques

• Accelerate with GPUs

Machine

Learning

F(x)

Deep

Learning


Infrastructure Impact on ML Performance

BRKINI-2021 67© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public22

WHY ARE GPUs GOOD FOR DEEP LEARNING?

GPUs deliver --- same or better prediction accuracy- faster results- smaller footprint- lower power- lower cost

Neural

NetworksGPUs

Inherently

Parallel

Matrix

Operations

FLOPS

Bandwidth

[Lee, Ranganath & Ng, 2007]


Many Machine Learning Algorithms


Many Machine Learning Algorithms

Each Algorithm has Many Parameters that Can Affect Performance:

Want to Try Combinations of Hyper Parameters


• Bigger Training Data Set -> Better Training

• Bigger Test Data Set -> Better Testing

• Limited Amount of Data

• Cross Validate to use all the data for both training and testing

• Pick the best performing model

Cross Validation: More Training Data Sets

Total Data Set

Train Test

TrainTest

TrainTest

Train Test


Number of Models to Train: Performance Matters

Number of ML

Algorithms: X5

Hyper Parameters: X10

TrainTest

TrainTest

Train Test

Cross Validation: X10

New Data: Start Over

Empirical Process:

Lots of Experiments

Required

Not Accurate: Troubleshoot

Unified Analytics Architecture with UCS


Cisco Unified Computing Systems Momentum

Source: 1 IDC, 2016 Q3, Nov 2016, Vendor Revenue Share

Source: As of Cisco Q2FY16 earnings results. Data Center Revenue is defined as Cisco UCS and Nexus 1000V

Integrated Infrastructure

(Cisco UCS, Nexus)

#1

Americas Revenue

Market Share in x86 Blades

#1

Global Revenue

Market Share in x86 Blades

#2

World Record Performance

Benchmark

130+

Total Storage Deployed in last

12 months

1 EXABYTE

Big Data Revenue

Growth in 4 Years

18x

Customers

60,000+

of Fortune 500 Have

Invested in UCS

>85%

HyperFlex Customers

2000+

BRKINI-2021 73


Architectural Challenges

Multiple

Management

Tools

Scale Up Converged Infrastructure

Hyperconverged

Infrastructure

Silos

Fabric

Sprawl

Complex Integration APIs APIs APIs APIs

Scale Out

X X X

Scale Out

Single Point of

Management

Scale Up Converged Infrastructure

Hyperconverged

No Silos

Unified

Fabric

Seamless

IntegrationOpen API

• Continual Optimization

• Automation

• End-to-End Security

Actual State Desired State


Why Cisco UCS for Big Data and Analytics

World Record Performance

#1 - TPCx-HS Benchmark

Easy to Scale

Unified Fabric for Network,

Storage, and Management Traffic

Simplified Management

Single Pane of Glass for

Thousands of Nodes

UCS Manager(Service Profiles)

Management

Ethernet

Storage

Proven Solutions

Validated Designs with Industry

Leaders

UCS 6200

Series Fabric

Interconnect

UCS Manager

16 Servers

Per Rack

• UCS Domain (160 Servers

(with FEX)

• 80 Servers direct connect)

• Manage by UCS Manager

• Up to 11.2 PB storage

• Multiple UCS Domains

• Interconnect using Nexus

7000/9000

• Scalable to 1000s of servers

• Centrally manage by UCS

Central

Scale from Small to Very Large Clusters


Management Simplicity

Simplified ScalabilityEasily Scale your infrastructure from few servers to thousands of servers with a fully Integrated Infrastructure

Centralized ManagementService Profiles for Servers• Manage all servers centrallyApplication Profiles for Network• Manage all network centrally

UCS Service Profile Cisco ACI Application Profile

Big Data: Management nightmare

Hundreds of Servers,

Thousands of management points


Cisco Intersight

Infrastructure-as-a-Service and Orchestration

Third Party Integrations: Infrastructure and Toolchains

Global Resource Pooling and Policy Management

Policy-Based Automation

Unified Element Management

UCS Capabilities Today

Transition to SaaS

SaaS Consumption Model

Free customers from care and feeding of management tools and eliminate upgrade dependencies

Seamless Extensibility

Simplify management across technologies and geography

Continuous Feature Integration

Rapid development, delivery and customer feedback

SaaS ModelCisco-Hosted CloudCustomer-Hosted CloudPartner-Hosted Cloud

Traditional Delivery ModelOn-prem software and HW-embedded tools


SaaS Simplicity

ActionableIntelligence

SaaS Delivered

Intuitive Experience

Enhanced Support

Proactive Guidance

Secure and Extensible

Analytics PoweredCisco Intersight

Centralized Management

Global Policies

Comprehensive Automation

Single Pane of Glass

Cisco Intersight

HSph-Hadoop Sort per Hour

This provides a normalized value of how much data is generated, sorted, and validated in one hour for the scale factor

(divide by 30 for a 30TB run). This is the performance of the system under test. Higher HSph is better

Price/HSph: Price per Performance

This divides the total cost of the System under test (inclusive of hardware, software, license cost, and 3 year 24x7x4

support) along with discount and divide by Performance (above HSpH). Lower price/performance is better

*As of 30-May-2016. Visit www.tpc.org for latest results

TPC Express Benchmark HS: Industry’s first standard for benchmarking big data systems to provide the industry with

verifiable performance, price-performance and availability metrics of hardware and software systems dealing with Big Data

TPC Members

Why Cisco UCS for Big Data? – Proven Performance

http://www.tpc.org/


What is a CVD?http://www.cisco.com/go/bigdata_design

It is a comprehensive co-validated design guide documented to facilitate faster, more reliable and more predictable customer deployments.

The document includes the solution overview, architecture design, configuration of different components involved, along with sizing and scaling guide. It includes every step involved from elevation diagrams to configuring storage, network, compute, operating system and application and Bill of materials.

http://www.cisco.com/go/bigdata_design


Mainstream Computing

Cisco UCS is a Revolutionary System Any Workload. Any Scale. One Platform.

HyperconvergedInfrastructure

Converged InfrastructureROBO

Fifth Generation UCS

HyperFlex Systems

UCS MiniE-Series

Scale Out

UCS S3000 Series

C-Series Rack Servers

UCS Integrated Infrastructure Solutions

Cisco UCS: It’s not a Server. It’s a System.

Unified Management, Single Control Plane, Single API

Edge Cloud


Enterprise Performance

Next Generation UCS M5 Portfolio Performance Optimized for Bare Metal, Virtualized, and Cloud Applications

Rac

kC240 M556 Cores – 20% more cores per

socket

100% more memory

80% more NVMe drives

Up to 2 GPUs

C220 M5

56 Cores – 20% more cores

per socket

100% more memory

80% more NVMe drives

B200 M5

100% more memory

55% more storage – 20.5TB

Up to 2 GPUs

Cloud Scale

S3260Ideal Capacity-Optimized

Platform for Large Object

Storage at Scale

(Purley Skylake refresh

targeting Q4’CY17)

Intensive/Mission Critical

C480 M5

Up to 6 GPUs

63% more drive bays

94% more NVMe support

B480 M5

Up to 4 GPUs

80% more storage – 39TB

20% better memory bandwidth


Summary

• Analytics is critical to business differentiation

• Working with lines of business in a collaborative, exploratory fashion is the key to successful big data and analytics project

• Multiple types of analytics

• Business

• Big Data

• IT Operations Analytics

• Internet of Things (IoT)

• Machine Learning / Deep Learning

Cisco has Analytics Solutions Based on UCS with

Management, Scale, and Performance

BRKINI-2021 84


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where do we go from here? lessons and landmarks from … · gateway with kafka spark streaming...

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