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Scalable and Emerging Information System Techniques
Scalable and Emerging Information System Techniques
Scalable and Emerging
www.ThesisScientist.com 1Big Data
• Big Data applies to information that can’t be
processed or analyzed using traditional
processes or tools.
www.ThesisScientist.com 2Techniques for Voluminous Data
• Cloud Computing is an efficient method to
balance between dealing with voluminous data
and keeping costs competitive, is designed to
deliver IT services consumable on demand, is
scalable as per user need and uses a pay-per-use
• Business houses are progressively turn towards
retaining core competencies, and shedding the
non-core competencies for on-demand
technology, business innovation and savings.
www.ThesisScientist.com 3Cloud Computing
• Cloud computing consists of hardware and
software resources made available on the
internet as managed third-party services.
• These services typically provide access to
advanced software applications and high-end
networks of server computers.
• Cloud computing is comparable to grid
computing, a type of computing where unused
processing cycles of all computers in a network
are connect to solve problems too intensive for
any stand-alone machine.
www.ThesisScientist.com 4Cloud Computing Layers
www.ThesisScientist.com 5Types of Cloud Computing
SaaS (Software as a Service): This is the idea of
providing a given application to multiple tenants,
typically using the browser which supports business
applications of host and delivery type as a service.
End Customers for instance Google Doc,
Common features of SaaS:
a) User applications run on cloud infrastructure
b) Accessible by users through web browser
c) Suitable for CRM (Customer Resource
d) Supports multi-tenant environment Types of Cloud Computing
PaaS (Platform as a Service): This is a variant of
SaaS. You run your own applications but you do it
on the cloud provider’s infrastructure. Provides a
comprehensive stack for developers to create
Cloud-ready business applications.
Developers for instance Force.com, Google App
Engine, Azure and Salesforce.com etc
Features of PaaS are:
a) Supports web-service standards
b) Dynamically scalable as per demand
c) Supports multi-tenant environment
www.ThesisScientist.com 7Types of Cloud Computing
IaaS (Infrastructure as a Service): These are virtual
storage and server options that organizations can
access on demand, even allowing the creation of
a virtual data center. Delivers computing
hardware like Servers, Network, Storage, etc. For
instance Rackspace.com, GoGrid.com etc.
Typical features are:
a)Users use resources but have no control of
underlying cloud infrastructure
b)Users pay for what they use
c)Flexible scalable infrastructure without
www.ThesisScientist.com 8Benefit of Cloud Computing
• Reduced Cost : Cloud technology is paid incrementally, saving
• Increased Storage: Organizations can store more data than on
private computer systems.
• Highly Automated: No longer do IT personnel need to worry
about keeping software up to date.
• Flexibility: Cloud computing offers much more flexibility than
past computing methods.
• More Mobility: Employees can access information wherever
they are, rather than having to remain at their desks.
• Allows IT to Shift Focus: No longer having to worry about
constant server updates and other computing issues,
government organizations will be free to concentrate on
• MapReduce is a software framework that
allows developers to write programs that
process massive amounts of unstructured
data in parallel across a distributed cluster of
processors or stand-alone computers.
• It was developed at Google for indexing Web
pages and replaced their original indexing
algorithms and heuristics in 2004.
www.ThesisScientist.com 10MapReduce and Hadoop Systems
• MapReduce is the heart of Hadoop.
• MapReduce allows data to be distributed
across a large cluster, and can distribute out
tasks across the data set to work on pieces of
it independently, and in parallel.
• This allows big data to be processed in
relatively little time.
• Apache has produced an open source
MapReduce platform called Hadoop.
www.ThesisScientist.com 11Framework is divided into two parts
• Map, a function that parcels out work to different
nodes in the distributed cluster.
• Reduce, another function that collates the work
and resolves the results into a single value.
• The MapReduce framework is fault-
tolerant because each node in the cluster is
expected to report back periodically with
completed work and status updates. If a node
remains silent for longer than the expected
interval, a master node makes note and re-
assigns the work to other nodes.
www.ThesisScientist.com 12MapReduce Interaction
Map functions create a user-defined “index” from source
• Reduce functions compute grouped aggregates based on
• Flexible framework
•users can cast raw original data in any model that they
•wide range of tasks can be expressed in this simple
• Developed by Apache as an open source
distributed MapReduce platform, based off of
• Runs on a Java architecture framework that
supports the processing of large data sets in a
distributed computing environment.
• Hadoop allows businesses to process large
amounts of data quickly by distributing the work
across several nodes.
• Good for Big data sets and on large cluster.
www.ThesisScientist.com 14Hadoop - A Key Business Tool
Hadoop System is used by Large Content-Distribution
Companies, such as...
• Hadoop is used for many of their tasks, and over 25,000
computers are running Hadoop.
• Hadoop is good for Amazon, they have lots of product data,
as well as user-generated content to index, and make
New York Times
• Hadoop is used to perform large-scale image conversions of
public domain articles.
www.ThesisScientist.com 15Hadoop - A Key Business Tool
Used by Non-Content-Distribution Companies, such as
Other early adopters include anyone with big data:
• medical records
• tax records
• network traffic
• large quantities of data
Wherever there is a lot of data, a Hadoop cluster can
generally process it relatively quickly.
www.ThesisScientist.com 16Data Management in the Cloud
• Data management applications are potential candidates for
deployment in the cloud
– Industry: enterprise database system have significant up-
front cost that includes both hardware and software costs
– Academia: manage, process and share mass-produced data
in the cloud
• Many “Cloud Killer Apps” are in fact data-intensive
– Batch Processing as with MapReduce
– Online Transaction Processing (OLTP) as in automated
– Offline Analytical Processing (OLAP) as in data mining or
www.ThesisScientist.com 17Data Management in the cloud
• A database system must implement for it to run
well in the cloud, in potential database applications
to consider for cloud deployment.
• Data management applications are best suited for
deployment on top of cloud computing
• Data management is the proper management of a
data resource for an organization. Data
management consists of a set of theories,
concepts, principles, and techniques for properly
• The primary objective is to support the business
information as needs of the organization.
www.ThesisScientist.com 18Data Management in Cloud
There are three characteristics of a cloud
• Compute power is elastic, but only if workload
• Data is stored at untrusted host.
• Data is replicated, often across large
www.ThesisScientist.com 19Information Retrieval
• Information retrieval is the activity of
obtaining information resources relevant to an
information need from a collection of
• Searches can be based on metadata or on full-
• Automated information retrieval systems are
used to reduce what has been called