intro to big data analytics and the hybrid cloud

© 2016 IBM Corporation

Introduction to Big Data, Analytics and the Hybrid

Cloud

© 2016 IBM Corporation2

About Me

Ian Balina- Big data visionary and story teller- 10+ years in software industry- Previous experience as software

developer and Deloitte consultant

Ian BalinaOpen Source Analytics Sales Evangelist

Retail, CPG and Travel Industry


Agenda

The story of Big Data- Hadoop

The emergence of Big Data Analytics- Spark

The birth of the Cloud- Hybrid Cloud


An overview on Big Data, Analytics and the Cloud

The story of Big Data

Expensive data warehouse

Commodity servers?

2.5 million items per minute

300,000 tweets per minute

200 million emails per minute 220,000 photos

per minute

5 TB per flight

> 1 PB per day gas turbines



The story of Big Data- Hadoop: reliable,

scalable, distributed computing and data storage




The emergence of Big Data Analytics- FAST DATA

#PerishableInsights

Insights that can provide exponentially more value than traditional analytics but the value expires and

evaporates once the moment is gone

Forrester: Mike Gualtieri, Principal Analyst

Value

Event

Action with traditional analytics

Immediate Action

Time

Lost Revenue




The emergence of Big Data Analytics- Spark: open source data processing engine

built for speed, ease of use, and sophisticated analytics

Logistic Regression in Hadoop & Spark

0

40

80

120Hadoop;

110

Spark; 0.9

HadoopSpark Graph Analytics

Fast and integrated graph computation

Stream Processing

Near real-time data processing & analytics

Machine Learning

Incredibly fast, easy to deploy algorithms

Unified Data Access

Fast, familiar query language for all data

Spar

k C

ore

Spark SQL

Spark Streaming

MLlib (machine learning)

GraphX (graph)


“Using IBM Analytics for Apache Spark, we can now give in-store teams valuable insight in seconds.”

—Ram Himmatraopet, Founder & CEO, SmarterData

Business challengeTo help its clients navigate the uncertainties of the digital-age retail industry, SmarterData wanted to find new ways to provide relevant, actionable, data-driven insights into consumer behavior.

TransformationSmarterData uses IBM Analytics for Apache Spark to deliver intelligent applications that combine operational and contextual data to help retailers understand consumers’ behavior and desires.

Helping retailers redefine practices for the digital ageBased in San Ramon, California, Smarter Data, Inc. leverages advanced data science technologies – predictive and prescriptive analytics – to help companies achieve relevance with their customers both online and in a retail environment, and manage the demands of digital-age business challenges.

Business benefits:

Empowers retailers with data-driven insights into consumer behavior, helping drive sales

Helps in-store teams provide smarter customer service based on real-time analysis

Leverages contextual data to predict individual needs and create personalized offers





The birth of the Cloud

Infrastructure as a Service

Code

Data

Runtime

Middleware

OS

Virtualization

Servers

Storage

Networking

Code

Data

Runtime

Middleware

OS

Virtualization

Servers

Storage

Networking

Platform as a Service

Code

Data

Runtime

Middleware

OS

Virtualization

Servers

Storage

Networking

Code

Data

Runtime

Middleware

OS

Virtualization

Servers

Storage

Networking

Software as a Service

Traditional IT – On-premise or Hosted

Customer Managed Service Provider Managed





The birth of the Cloud- Hybrid Cloud Private

ManagedPrivate

HostedPrivate PublicEnterprise

Hybrid CloudIntegration

EnterpriseData Center

EnterpriseData Center

IBMSO

SoftLayerAnd IBM SO

Enterprise UsersEnterpriseData Center


A US grocery store chain uses business intelligence to identify insights that help make a proof of concept detailed and convincing

Business challenge: The CEO of this grocery store chain knew that analytics and cloud-based computing were going to help take the company to the next level by guiding marketing and merchandising decisions, but he needed to convince key stakeholders. His team came to IBM for help developing a proof of concept.

The smarter solution: The company used a business intelligence and predictive modeling solution to develop a detailed and groundbreaking understanding of the link between weather and grocery shopping behavior in its US stores. By demonstrating that analytics can provide insight into which items it should procure, feature and market during which kinds of weather, the company not only convinced stakeholders of the value of analytics but also gained valuable new insight into its business.

Using big data to anticipate the ebbs and flows of demand holds tremendous potential in the grocery store industry in terms of procurement, merchandising and staffing.

Half the costof similar projects, thanks to a cloud-based infrastructure

75% fastercompletion of proof of concept than anticipated

Successfulin convincing stakeholders of the value of cloud-based analytics

intro to big data analytics and the hybrid cloud

Data & Analytics