Big Data New
Big Data New
Big Data New
• Big data is the term for a collection of data sets so large and
complex that it becomes difficult to process using on-hand
database management tools or traditional data processing
applications.
• The challenges include capture, curation, storage, search,
sharing, transfer, analysis, and visualization.
• The trend to larger data sets is due to the additional
information derivable from analysis of a single large set of
related data, as compared to separate smaller sets with the
same total amount of data, allowing correlations to be found
to "spot business trends, determine quality of research,
prevent diseases, link legal citations, combat crime, and
determine real-time roadway traffic conditions.”
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Big Data: 3V’s
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Volume (Scale)
• Data Volume
– 44x increase from 2009 2020
– From 0.8 zettabytes to 35zb
• Data volume is increasing exponentially
Exponential increase in
collected/generated data
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4.6
30 billion RFID billion
tags today
12+ TBs (1.3B in 2005)
camera
of tweet data phones
every day world wide
100s of
millions
data every day
of GPS
? TBs of
enabled
devices sold
annually
25+ TBs of 2+
log data
every day billion
people on
the Web
76 million smart meters by end
in 2009… 2011
200M by 2014
CERN’s Large Hydron Collider (LHC) generates 15 PB a year
Maximilien Brice, © CERN
The Earthscope
• The Earthscope is the world's largest
science project. Designed to track
North America's geological evolution,
this observatory records data over 3.8
million square miles, amassing 67
terabytes of data. It analyzes seismic
slips in the San Andreas fault, sure, but
also the plume of magma underneath
Yellowstone and much, much more.
(http://www.msnbc.msn.com/id/4436
3598/ns/technology_and_science-
future_of_technology/#.TmetOdQ--uI)
Variety (Complexity)
• Relational Data (Tables/Transaction/Legacy Data)
• Text Data (Web)
• Semi-structured Data (XML)
• Graph Data
– Social Network, Semantic Web (RDF), …
• Streaming Data
– You can only scan the data once
Social Banking
Media Finance
Our
Gaming
Customer Known
History
Purchas
Entertain
e
Velocity (Speed)
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Real-time/Fast Data
Mobile devices
(tracking all objects all the time)
• The progress and innovation is no longer hindered by the ability to collect data
• But, by the ability to manage, analyze, summarize, visualize, and discover
knowledge from the collected data in a timely manner and in a scalable fashion
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Real-Time Analytics/Decision Requirement
Product
Recommendations Learning why Customers
Influence
that are Relevant Behavior Switch to competitors
& Compelling and their offers; in
time to Counter
Friend Invitations
Improving the Customer to join a
Marketing Game or Activity
Effectiveness of a that expands
Promotion while it business
is still in Play
Preventing Fraud
as it is Occurring
& preventing more
proactively
Some Make it 4V’s
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Harnessing Big Data
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The Model Has Changed…
• The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming data
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What’s driving Big Data
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THE EVOLUTION OF BUSINESS INTELLIGENCE
Interactive Business
Speed
Intelligence & Big Data:
In-memory RDBMS Scale
Real Time &
Single View
BI Reporting QliqView, Tableau, HANA
OLAP &
Graph Databases
Dataware house
Business Objects, SAS, Big Data: Speed
Scale
Informatica, Cognos other SQL Batch Processing &
Reporting Tools
Distributed Data Store
Hadoop/Spark; HBase/Cassandra
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Big Data Technology
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Cloud Computing
• IT resources provided as a service
– Compute, storage, databases, queues
• Clouds leverage economies of scale of
commodity hardware
– Cheap storage, high bandwidth networks &
multicore processors
– Geographically distributed data centers
• Offerings from Microsoft, Amazon, Google, …
wikipedia:Cloud Computing
Benefits
• Cost & management
– Economies of scale, “out-sourced” resource
management
• Reduced Time to deployment
– Ease of assembly, works “out of the box”
• Scaling
– On demand provisioning, co-locate data and compute
• Reliability
– Massive, redundant, shared resources
• Sustainability
– Hardware not owned
Types of Cloud Computing
• Public Cloud: Computing infrastructure is hosted at the
vendor’s premises.
• Private Cloud: Computing architecture is dedicated to the
customer and is not shared with other organisations.
• Hybrid Cloud: Organisations host some critical, secure
applications in private clouds. The not so critical applications
are hosted in the public cloud
– Cloud bursting: the organisation uses its own infrastructure for normal
usage, but cloud is used for peak loads.
• Community Cloud
Classification of Cloud Computing
based on Service Provided
• Infrastructure as a service (IaaS)
– Offering hardware related services using the principles of cloud
computing. These could include storage services (database or disk
storage) or virtual servers.
– Amazon EC2, Amazon S3, Rackspace Cloud Servers and Flexiscale.
• Platform as a Service (PaaS)
– Offering a development platform on the cloud.
– Google’s Application Engine, Microsofts Azure, Salesforce.com’s
force.com .
• Software as a service (SaaS)
– Including a complete software offering on the cloud. Users can
access a software application hosted by the cloud vendor on pay-
per-use basis. This is a well-established sector.
– Salesforce.coms’ offering in the online Customer Relationship
Management (CRM) space, Googles gmail and Microsofts hotmail,
Google docs.
Infrastructure as a Service (IaaS)
More Refined Categorization
• Storage-as-a-service
• Database-as-a-service
• Information-as-a-service
• Process-as-a-service
• Application-as-a-service
• Platform-as-a-service
• Integration-as-a-service
• Security-as-a-service
• Management/
Governance-as-a-service
• Testing-as-a-service
• Infrastructure-as-a-service
InfoWorld Cloud Computing Deep Dive
Key Ingredients in Cloud Computing
• Service-Oriented Architecture (SOA)
• Utility Computing (on demand)
• Virtualization (P2P Network)
• SAAS (Software As A Service)
• PAAS (Platform AS A Service)
• IAAS (Infrastructure AS A Servie)
• Web Services in Cloud
Enabling Technology: Virtualization
Hardware Hardware
COBOL, Amazon.com
Edsel ARPANET Internet
VMs
Flat File Storage
AppEngine: EC2/S3:
• Higher-level functionality • Lower-level functionality
(e.g., automatic scaling) • More flexible
• More restrictive • Coarser billing model
(e.g., respond to URL only)
• Proprietary lock-in
Google AppEngine vs. Amazon
June 3, 2008 Slide 35
EC2/S3