intro to big data analytics and the hybrid cloud
TRANSCRIPT
© 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
© 2016 IBM Corporation3
Agenda
The story of Big Data- Hadoop
The emergence of Big Data Analytics- Spark
The birth of the Cloud- Hybrid Cloud
© 2016 IBM Corporation4
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
© 2016 IBM Corporation5
An overview on Big Data, Analytics and the Cloud
The story of Big Data- Hadoop: reliable,
scalable, distributed computing and data storage
© 2016 IBM Corporation6
An overview on Big Data, Analytics and the Cloud
The story of Big Data- Hadoop
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
© 2016 IBM Corporation7
An overview on Big Data, Analytics and the Cloud
The story of Big Data- Hadoop
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)
© 2013 IBM Corporation8
“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
© 2016 IBM Corporation9
An overview on Big Data, Analytics and the Cloud
The story of Big Data- Hadoop
The emergence of Big Data Analytics- Spark
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
© 2016 IBM Corporation10
An overview on Big Data, Analytics and the Cloud
The story of Big Data- Hadoop
The emergence of Big Data Analytics- Spark
The birth of the Cloud- Hybrid Cloud Private
ManagedPrivate
HostedPrivate PublicEnterprise
Hybrid CloudIntegration
EnterpriseData Center
EnterpriseData Center
IBMSO
SoftLayerAnd IBM SO
Enterprise UsersEnterpriseData Center
© 2016 IBM Corporation11
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