S ba0881 big-data-use-cases-pearson-edge2015-v7

© Copyright IBM Corporation 2015Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM.

sBA0881

What Is Big Data? Architectures and Practical Use Cases

Tony Pearson

Master Inventor and Senior IT Specialist

IBM Corporation

© Copyright IBM Corporation 2015

Abstract

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Do you understand the storage implications of big data analytics?

This session will explain what big data is, provide some practical use

cases, then explain the IBM products that support big data


This week with Tony Pearson

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Day Time Topic

Monday 10:30am Software Defined Storage -- Why? What? How? (repeats Tuesday)

03:00pm IBM's Cloud Storage Options (repeats Wednesday)

04:30pm Data Footprint Reduction – Understanding IBM Storage Efficiency Options

Tuesday 10:30am Software Defined Storage -- Why? What? How?

12:30pm What Is Big Data? Architectures and Practical Use Cases

01:45pm IBM Smarter Storage Strategy (repeats Wednesday)

Wednesday 09:00am New Generation of Storage Tiering: Less Management Lower Investment and Increased Performance

10:30am IBM Smarter Storage Strategy

12:30pm IBM's Cloud Storage Options

01:45pm IBM Spectrum Scale (Elastic Storage) Offerings

Thursday 12:30pm The Pendulum Swings Back -- Understanding Converged and Hyperconverged Environments

05:45pm Storage Meet the Experts

Friday 09:00am IBM Spectrum Storage Integration with OpenStack

What is Big Data?Big Data Use CasesIBM Analytics PlatformIBM Spectrum Scale

Agenda


What is Big Data?

Data sets so large and complex that it becomes difficult to process using relational databases

The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization

Analysis of a single large set of related data allows correlations to be found

Can be used to identify trends, patterns and insights to make better decisions

Source: Wikipedia

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OLAP cube

ExtractTransformLoad (ETL)

Strategic planning

based on historical analysis andspeculation

Day-to-day operations based on

reports, news, intuition

Business Executives

Make decisions3

Traditional Decision Making Process

Reports

BatchProcessing

Transaction and Application data

Database Administrators

System of Record

Gather data1

Business Analysts

Analyze2

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What has Changed in the Last Few Decades?

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1986 2015

6%

99%

Analogdata

Digitaldata


Machinedata

Social media, email

Enterprisecontent

20%Structured data

80%Unstructured data


New Sources of Data to Analyze –the Four V’s of big data

• Volume

• Scale of data has grown beyond relational database capabilities

• Variety

• Machine data, enterprise content, and social media and email

• Velocity

• Computing has advanced to receive and analyze real-time data streams

• Veracity

• How much can you trust the data is right and accurate?


DatabaseAdministrators

System of Record

System of Engagement

System of Insight

MachineData,log data

Socialmedia,photos,audio,video, email

Enterprisecontent

StorageAdministrators

Gather and Identify sources of data1

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Data is the New Oil

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DATA is the

new OILIn its raw form,

oil has little value…

Once processed and refined,

it helps to power the world!


Structured, Repeatable,

Linear

OLAP cube

Unstructured,Exploratory,

Iterative

New Capabilities to Analyze the Data

Reports Visualization and Discovery

Hadoop

Data warehousing

Stream Computing

Integration and Governance

Text Analytics

BusinessAnalyst

DataScientist

Analyze data2

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What does a Data Scientist do?

• “It’s no longer hard to find the answer to a given question; the hard part is finding the right question. And as questions evolve, we gain better insight into our ecosystem and our business.”

-- Kevin Weil, Lead Analyst at Twitter

• A data scientist must have…

• Strong business acumen

• Modeling, statistics, analytics and math skills

• Ability to communicate findings, tell a story from the data, to both business and IT leaders

• Inquisitive: exploring, doing “what if?” analyses, questioning existing assumptions and processes to spot trends, patterns and hidden insight.

Computers are useless.

They can only give you answers.

– Pablo Picasso

Source: http://www-01.ibm.com/software/data/infosphere/data-scientist/http://blog.cloudera.com/blog/2010/09/twitter-analytics-lead-kevin-weil-and-a-presenter-at-hadoop-world-interviewed/

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Data �� Information �� Knowledge �� Wisdom (DIKW)

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Wisdom

Applied I better stop the car!

Knowledge

ContextThe traffic light I am driving towards has

turned red

InformationMeaning

South-facing light at corner of Pitt and George

streets has turn red

DataRaw

červený685 nm, 421 THz,

#FF0000

http://legoviews.com/2013/04/06/put-knowledge-into-action-and-enhance-organisational-wisdom-lsp-and-dikw/


Better Decisions for New Business Outcomes

Day-to-day

operations based on real-time

analytics

Strategic planning

based on science, trends, patterns

and insight

Know Everything about your Customers

Innovate new products at Speed and Scale

Instant Awareness of Fraud and Risk

Exploit Instrumented Assets

Run Zero-latency Operations

BusinessExecutive

Make Decisions and Take Action

3

EmpoweredEmployees

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statisticalmodels

Decision Making Process in the Era of big data

Real-timeAnalytics

Database Administrators

System of Insight

Strategic planningbased on science,

trends, patterns and insight

Dashboard

StorageAdministrators

Gather and Identify sources of data1

Day-to-day operations based

on real-time analytics

Business ExecutivesEmpowered Employees

Make Decisions and Take Action

3DataScientists

BusinessAnalysts

Analyze data2

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Agenda


Practical Use Cases – The Analytics Landscape

Degree of Complexity

Com

petitive A

dvanta

ge

Standard Reporting

Ad hoc reporting

Query/drill down

Alerts

Simulation

Forecasting

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What if these trends continue?

What could happen…. ?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Based on: Competing on Analytics, Davenport and Harris, 2007

Descriptive

Prescriptive

Predictive

How can we achieve the best outcome?

How can we achieve the best outcome including the effects of variability?

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Innovate New Products and Services at Speed and Scale

Vestas, the world’s largest wind energy company, was able to use

big data and IBM technology to increase wind power generation through optimal turbine placement.

Reducing the time to analyze petabytes of data with IBM Big Insights software and IBM Spectrum Scale

“Before, it could take us three weeks to get a response to some of our questions simply because we had to process a lot of data. We expect that we can get answers for the same questions now in 15 minutes.” – Lars Christian Christensen

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If You are Not Paying for it…Then you are not the Customer, … You are the Product Being Sold!

• How much is each user worth to Social Media companies?

Sources: Geek & Poke comic, “Let’s Talk about Data” by Neha Mehta

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Social Network PublicDatabase

How valuable is Amy to my retail sales? Who does she influence? What do they spend?

Reta

iler

Amy Bearn

32, Married, mother of 3,Accountant

Telco Score: 91CPG Score: 76Fashion Score: 88

Telc

oco

mp

an

y

How valuable is Amy to my mobile phone network? How likely is she to switch carriers? How many other customers will follow

Merged Network

Calling Network

360 Degree View of the Customer –A Demographic of One

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Deep Individual Customer Insight• Preferences• Interests• Likes

Run Zero-Latency Operations

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Direct Channel Workflow Enrich

Initiate Direct

Response

Initiate Channel

Response

Initiate Process or Workflow

Enrich Customer

Profile

Real-timeDecision


How Target® Figured Out a Teen Girl Was Pregnant Before Her Father Did

• Every time you go shopping, you share intimate details about your consumption patterns with retailers.

• Target has figured out how to data-mine whether you have a baby on the way

• Looked at historical buying data for all the ladies who had signed up for Target baby registries

• Unscented soaps and lotions

• Calcium, magnesium and zinc supplements

• About 25 products help generate “pregnancy prediction” score and her “baby due date”

• Target sends coupons timed to very specific stages of her pregnancy

Source: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

“My daughter got this in the mail. She’s still in high school, and you’re sending her coupons for baby clothes and cribs?”

-- Angry father of teen girl

“I had a talk with my daughter,…She’s due in August. I owe you an apology.”

-- Same father, 3 days later

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Exploit Instrumented Assets

Doctors from University of Ontario apply big data to neonatal infant monitoring to predict infection

Detect Neonatal Patient Symptoms

Up to 24 Hours sooner

Continuously correlate data

Thousands of events each second

Signal Processing and Data Cleansing

Heart Rate Variability

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Agenda

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The IBM big data platform advantage

BI / Reporting

BI / Reporting

Exploration / Visualization

FunctionalApp

IndustryApp

Predictive Analytics

Content Analytics

Analytic Applications

IBM big data platform

Systems Management

Application Development

Visualization & Discovery

Accelerators

Information Integration & Governance

HadoopSystem

Stream Computing

Data Warehouse

• The platform provides benefit as you move from an entry point to a second and third project

• Shared components and integration between systems lowers deployment costs

• Key points of leverage• Reuse text analytics across streams and

BigInsights

• Hadoop connectors between Streams and Information Integration

• Common integration, metadata and governance across all engines

• Accelerators built across multiple engines – common analytics, models, and visualization


Simplify your data warehouse

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• Customer Need• Business users are hampered by the poor

performance of analytics of a general-purpose enterprise warehouse – queries take hours to run

• Enterprise data warehouse is encumbered by too much data for too many purposes

• Need to ingest huge volumes of structured data and run multiple concurrent deep analytic queries against it

• IT needs to reduce the cost of maintaining the data warehouse

• Value Statement• Speed and Simplicity for deep analytics

• 100s to 1000s users/second for operation analytics

• Customer examples• Catalina Marketing – executing 10x the amount

of predictive workloads with the same staff

System for Transactions

System for Analytics

System for Operational Analytics

Get started with IBM PureData Systems!


Ad-Hoc versus Operational Analytics

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Analyze streaming data in Real time

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• Customer Need• Harness and process streaming data

sources

• Select valuable data and insights to be stored for further processing

• Quickly process and analyze perishable data, and take timely action

• Value Statement• Significantly reduced processing time and

cost – process and then store what’s valuable

• React in real-time to capture opportunities before they expire

• Customer examples• Ufone – Telco Call Detail Record (CDR)

analytics for customer churn prevention

Get started with IBM Streams!

Visualization

Streams Runtime

Deployments

Sync

Adapters

Analytic

Operators

Source

Adapters

Automated

and

Optimized

Deployment

Streaming Data

Sources

Streams Studio IDE

Dominant Players vs. Contender platforms

OS Tape Cloud Management

Big Data & Analytics

DominantPlayer

Microsoft Windows

Quantum DLT

Amazon Web Services

Cloudera

Contender platform

Linux Linear Tape Open (LTO)

OpenStack Open Data Platform

Supporters of Contenderplatform

IBM, RedHat, SUSE, Oracle andothers

IBM, HP, Certance and others

IBM, HP, Rackspace, RedHat, Dell, Cisco, VMware and others

IBM, Pivotal,Hortonworks and others

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� IBM InfoSphere BigInsights is a 100% standard Hadoop distribution� By default, open source components are always deployed� Elect to use proprietary capabilities depending on your needs� In some cases, proprietary capabilities offer significant benefits

Open standards first, but with freedom of choice

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HDFS

YARN

HIVE

MapReduce

PIG

SpectrumScale

PlatformSymphony

Big SQL

AdaptiveMapReduce

BigSheets

Share data with non-Hadoop applications and simplify data management

Re-use existing tools and expertise, Avoid additional development costs

Boost performance, support time-critical workloads, do more with less

True multi-tenancy to boost service levels and avoid duplication on infrastructure

Simplify access for end-users, minimize software development


Text Analytics

Spectrum Scale Platform SymphonyIBM BigInsights

Enterprise Management

System ML on Big R

Distributed R

IBM Open Platform with Apache Hadoop

IBM BigInsights Data Scientist

IBM BigInsights Analyst

Big SQL

Big Sheets

Big SQL

BigSheets

IBM BigInsights for Apache Hadoop

IBM BigInsights for Apache Hadoop

Three new user-centric modules founded on an Open Data Platform

IBM Open Platform with Apache Hadoop is IBM’s own 100% open source Apache Hadoop distribution. IBM will include the ODP common kernel when available.

Business Analyst

Data Scientist

Administrator

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Platform Symphony Integrates with Hadoop

• YARN uses a pluggable architecture for schedulers. • FIFO, Fair, and Capacity Schedulers implemented this way

• Symphony EGO is also implemented this way.

• Therefore, scheduler is completely transparent to YARN Applications.

• ISV Certification for Platform Symphony is not required.

YARN (open source)

Fair CapacitySymphony

EGOFIFO

Like other schedulers, queues and policies are defined in Platform Symphony EGO.

App1 App2 App3

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IBM InfoSphere BigInsights – Big SQL

Native Hadoop Data Sources

CSV SEQ Parquet RC

AVRO ORC JSON Custom

Optimized SQL MPP Run-time

Big SQL

SQL based Application

� IBM’s SQL for Hadoop

• Makes Hadoop data accessible to a wider audience

• Familiar, widely known syntax

• Leverage native Hadoop data sources

� Complements the Data Warehouse

• Exploratory analytics

• Sandbox, Data Lake

� Included in IBM BigInsights

� Use familiar SQL tools

• Cognos, SPSS, Tableau, MicroStrategy

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Information Ingestion and Operational Information

Decision Management

BI and Predictive Analytics

Navigation and Discovery

IntelligenceAnalysis

Landing Area,Analytics Zoneand Archive

� Raw Data� Structured Data� Text Analytics� Data Mining� Entity Analytics� Machine Learning

Real-timeAnalytics

� Video/Audio� Network/Sensor� Entity Analytics� Predictive

Exploration,Integrated Warehouse, and Mart Zones

� Discovery� Deep Reflection� Operational� Predictive

� Stream Processing � Data Integration � Master Data

Streams

Information Governance, Security and Business Continuity

Architecture Pattern for big data Implementation

ApplicationTransaction

Machinedata

Social media, email

Enterprisecontent

Data at Rest

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Agenda


Why use IBM Spectrum Scale™

Extreme Scalability

� Add or Remove nodes and storage, without disruption or performance impact to applications

Universal Access to Data

� All servers and clients have access to data through a variety of file and object protocols

High Performance

� Parallel access with no hot spots

Proven Reliability

� Used by over 200 of the top 500 Supercomputers

� Survive any node or storage failure with Distributed RAID and redundant components

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Hadoop Analytics – HDFS vs IBM Spectrum Scale™

HDFSSaveResults

DiscardR

est *

IBM Hadoop Connector allows

Map/Reduce programs to process

data without application changes

IBM Spectrum Scale

Application data stored on IBM Spectrum Scale is readily available for analytics

SaveResults

JFS2

NTFS

EXT4

Data Sources mashup of structured and unstructured data from a variety of sources

Actionable InsightsProvides answers to the

Who, What, Where, When, Why and How

Business Intelligence & Predictive Analytics> Competitive Advantages> New Threats and Fraud

> Changing Needs and Forecasting

> And More!

35* Discarding HDFS data is optional step

HDFS versus IBM Spectrum Scale™

Hadoop HDFS

HDFS NameNode HA added in version 2.0. NameNode HA in active/passive configuration

Difficulty to ingest data – special tools required

Lacking enterprise readiness

No single point of failure, distributed metadata in active/active configuration since

1998

Ingest data using policies for data placement

Versatile, Multi-purpose,Hybrid Storage (locality and shared)

Enterprise ready with support for advanced storage features (Encryption, DR, replication,

SW RAID etc)

Large block-sizes – poor support for small files Variable block sizes – suited to multiple types

of data and metadata access pattern

Scale compute and storage independently(Policy based ILM)

Compute and Storage tightly coupled –leading to very low CPU utilization

Single-purpose, Hadoop MapReduce only

POSIX file system – easy to use and manageNon-POSIX file system – obscure commands.

Does not support in-place updates.

IBM Spectrum Scale

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HDFSNamenode

SecondaryNamenode

IBM Spectrum Scale™ – File Placement Optimization

SAN

Internal, Direct-Attach

TCP/IP or RDMA Network

• Spectrum Scale avoids the need for a central namenode, a common failure point in HDFS

• Avoid long recovery times in the event of namenode failure

• Spectrum Scale can intermix FPO with standard NSD server and client nodes in the same cluster

• POSIX compliance which is key to avoid data islands.

• Robustness and performance at massive scale and maturity

File Placement Optimization (FPO)

Creates a “shared nothing” cluster similar to HDFS in Hadoop environments

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Share-Nothing versus Shared-Disk Deployments

DataData

Data Parity

DataData

Data

CopyCopy

Copy

CopyCopy

CopyTCP/IPor RDMA

Need more compute? Add another node!

Spectrum Scale and Elastic Storage Server reduce storage to one

RAID-protected copy of the data

Scale compute and storage capacity separately

Spectrum Scale FPO can keep 1,2 or 3

replicas of the data

Need more storage capacity?

Add another node!

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3x versus 1.3x

TCP/IPor RDMA


IBM Spectrum Scale™ –Software, Systems or Cloud Services

Software

• Install software on your own choice of Industry standard x86 or POWER servers

Pre-built Systems

• Elastic Storage Server with distributed RAID

• Storwize V7000 Unified

Cloud Services

• Spectrum Scale can be deployed on any Cloud

Scale

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Session summary

• Big data is being generated by everything around us

• Every digital process and social media exchange produces it

• Systems, sensors and mobile devices transmit it

• Big data is arriving from multiple sources at amazing velocities, volumes and varieties

• To extract meaningful value from big data, you need optimal processing power, storage, analytics capabilities, and skills

Sources: The Economist, and special thanks toDr. Bob Sutor, IBM VP, Business Solutions & Mathematical Sciences


Big Data & Analytics

Building Big Data and Analytics Solutions in the Cloudhttp://www.redbooks.ibm.com/abstracts/redp5085.html?Open

o IBM BigInsightso IBM PureData System for Hadoopo IBM PureData System for Analyticso IBM PureData System for Operational Analyticso IBM InfoSphere Warehouseo IBM Streamso IBM InfoSphere Data Explorer (Watson Explorer)o IBM InfoSphere Data Architecto IBM InfoSphere Information Analyzero IBM InfoSphere Information Servero IBM InfoSphere Information Server for Data Qualityo IBM InfoSphere Master Data Management Familyo IBM InfoSphere Optim Familyo IBM InfoSphere Guardium Family

“Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models.”

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Research Paper

“In this paper, we revisit the

debate on the need of a new non-

POSIX storage stack for cloud

analytics and argue, based on an

initial evaluation, that it can be built on traditional POSIX-based cluster filesystems.“ 44


Hadoop for the Enterprise

http://www.ibm.com/software/data/infosphere/hadoop/enterprise.html

IBM BigInsights for Apache Hadoop provides a 100% open source platform and offers analytic and enterprise capabilities for Hadoop.

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46

IBM Tucson Executive Briefing Center

• Tucson, Arizona is home for storage hardware and software design and development

• IBM Tucson Executive

Briefing Center offers:

• Technology briefings

• Product demonstrations

• Solution workshops

• Take a video tour!

• http://youtu.be/CXrpoCZAazg

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About the Speaker

Tony Pearson is a Master Inventor and Senior managing consultant for the IBM System Storage™ product line. Tony joined

IBM Corporation in 1986 in Tucson, Arizona, USA, and has lived there ever since. In his current role, Tony presents briefings

on storage topics covering the entire System Storage product line, Tivoli storage software products, and topics related to Cloud

Computing. He interacts with clients, speaks at conferences and events, and leads client workshops to help clients with

strategic planning for IBM’s integrated set of storage management software, hardware, and virtualization products.

Tony writes the “Inside System Storage” blog, which is read by hundreds of clients, IBM sales reps and IBM Business Partners

every week. This blog was rated one of the top 10 blogs for the IT storage industry by “Networking World” magazine, and #1

most read IBM blog on IBM’s developerWorks. The blog has been published in series of books, Inside System Storage:

Volume I through V.

Over the past years, Tony has worked in development, marketing and customer care positions for various storage hardware

and software products. Tony has a Bachelor of Science degree in Software Engineering, and a Master of Science degree in

Electrical Engineering, both from the University of Arizona. Tony holds 19 IBM patents for inventions on storage hardware and

software products.

9000 S. Rita Road

Bldg 9032 Floor 1

Tucson, AZ 85744

+1 520-799-4309 (Office)

[email protected]

Tony Pearson

Master Inventor,

Senior IT Specialist

IBM System Storage™


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Email:[email protected]

Twitter:twitter.com/az99Øtony

Blog: ibm.co/Pearson

Books:www.lulu.com/spotlight/99Ø_tony

IBM Expert Network on Slideshare:www.slideshare.net/az99Øtony

Facebook:www.facebook.com/tony.pearson.16121

Linkedin:www.linkedin.com/profile/view?id=103718598

Additional Resources from Tony Pearson


Continue growing your IBM skills

ibm.com/training provides acomprehensive portfolio of skills and careeraccelerators that are designed to meet all your training needs.

• Training in cities local to you - where and when you need it, and in the format you want• Use IBM Training Search to locate public training classes

near to you with our five Global Training Providers

• Private training is also available with our Global Training Providers

• Demanding a high standard of quality –view the paths to success• Browse Training Paths and Certifications to find the

course that is right for you

• If you can’t find the training that is right for you with our Global Training Providers, we can help.• Contact IBM Training at [email protected]

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Global Skills Initiative


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Information concerning non-IBM products was obtained from a supplier of these products, published announcement material, or other publicly available sources and does not constitute an endorsement of such products by IBM. Sources for non-IBM list prices and performance numbers are taken from publicly available information, including vendor announcements and vendor worldwide homepages. IBM has not tested these products and cannot confirm the accuracy of performance, capability, or any other claims related to non-IBM products. Questions on the capability of non-IBM products should be addressed to the supplier of those products.

All statements regarding IBM future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

Some information addresses anticipated future capabilities. Such information is not intended as a definitive statement of a commitment to specific levels of performance, function or delivery schedules with respect to any future products. Such commitments are only made in IBM product announcements. The information is presented here to communicate IBM's current investment and development activities as a good faith effort to help with our customers' future planning.

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