Big data refers to massive amounts of structured and unstructured data that is difficult to process using traditional databases. It is characterized by volume, variety, velocity, and veracity. Major sources of big data include social media posts, videos uploaded, app downloads, searches, and tweets. Trends in big data include increased use of sensors, tools for non-data scientists, in-memory databases, NoSQL databases, Hadoop, cloud storage, machine learning, and self-service analytics. Big data has applications in banking, media, healthcare, energy, manufacturing, education, and transportation for tasks like fraud detection, personalized experiences, reducing costs, predictive maintenance, measuring teacher effectiveness, and traffic control.

1. DATA NETWORK AND SECURITY
Trends, Applications & Security
Shahbaz Ahmad Anjam
BSITF15MM020
BSIT (5th
) UOS (Mianwali

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Big Data
Big Data word mean a massive volume of both structured and
unstructured data that is so large it is difficult to process using
traditional database and software techniques. In most enterprise
scenarios the volume of data is too big or it moves too fast or it
exceeds current processing capacity.
Big Data in a another term
Volume, variety, velocity, and veracity
 Volume
The quantity of generated and stored data. The quantity of data
flowing to the system within certain time
 Variety
The type and nature of the data. Incoming quantity of data and
types.

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 Velocity
The speed at which the data is generated and processed to meet
the demands and challenges that lie in the path of growth and
development.
 Veracity
The data quality of captured data can vary greatly, affecting the
accurate analysis.
Big Sources of Big Data
Some data sources that generate huge data volumes in just 60 seconds.
 More than 60 blog and 1500 blog posts are done.
 More than 70 domains are registered.
 More than 600 new videos are uploaded on you tube.
 More than 40 questions asked on yahoo are answered and 100 + questions
answered on answers.com
 More than 1300 iPhone applications are downloaded every minute.
 694445 searches are made on Google every minute.
 About 320 accounts are created on Twitter and more than 98000 tweets are
made every minute.
Trends
Big Data trends shift rapidly, but experts expect machine learning, predictive
analytics, IoT and edge computing to have a big impact on big data projects in the
years ahead.
Researchers at Forrester have "found that, in 2016, almost 40 percent of firms
are implementing and expanding big data technology adoption. Another 30 percent
are planning to adopt big data in the next 12 months." Similarly, the Big Data
Executive Survey 2016 from New Vantage Partners found that 62.5 percent of

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firms now have at least one big data project in production, and only 5.4 percent of
organizations have no big data initiatives planned or underway.
2015 Trends
 More Sensor Driven Data
This would be sensor-to-sensor data being collected,
collated and analyzed through purely sensor based collection.
This can be done in multiple ways from the way that objects
are interacting with another object, to the settings that
people are using on particular devices.
 Non-Data Scientists
Automated platforms that can allow employees who may not
have as much skill with data as others, to collect, analyze
and make decisions based on this data. This could be
anything from simple to use interfaces with more complex
backends or simpler tasks that could create business
results.
 In-Memory Databases
Databases allow companies the freedom to access, analyze
and take actions based on data much quicker than regular
databases. This in turn means that either decisions can be
made quicker as data can be analyzed faster or more
informed as more data can be analyzed in the same amount
of time.

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2016 Trends
 The NoSQL
NoSQL technologies, commonly associated with unstructured
data, have seen significant adoption over the last 12 months.Going
forward, the shift to NoSQL databases as a leading piece of the
enterprise IT landscape becomes clear as the benefits of
schema-less database concepts become more pronounced.
 Hadoop
Hadoop is an open source software framwork used for
distributed storage and processing of dataset of big data
using Map reduce programing model.
The Hadoop includes four modules: Hadoop Common
(utilities), Hadoop Distributed File System (HDFS), Hadoop
YARN (scheduler) and Hadoop MapReduce (parallel
processing).
 IoT
IoT has been touted for smart homes, wearables, smart
cities, smart grids, industrial internet, connected vehicles,
connected health, smart retail, agriculture, and a host of
other scenarios. The Internet of Things (IoT) is a system of
interrelated computing devices, mechanical and digital
machines, objects, animals or people that are provided with
unique identifiers and the ability to transfer data over a
network without requiring human-to-human or human-to-
computer interaction.

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 Cloud Storage
Cloud storage enables applications to upload data to a network of
remote, connected servers. Applications can then maintain that data
and access it from anywhere. Applications access data using a web-
based API that works with client applications.
Storage is available in four main types:
 Personal storage: Services that enable individuals to store data and
sync it across multiple devices.
 Public storage: A cloud storage provider that fully manages data for an
enterprise offsite.
 Private storage: The cloud storage provider works on premises at an
organization’s data center.
 Hybrid storage: A mix of public and private cloud storage.
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2017 Big Data Trends
Artificial Intelligence is most common trend of 2017
 Neural networks
A neural network is a system of hardware and/or software
patterned after the operation of neurons in the human brain. Neural
networks -- also called artificial neural networks --Neural networks
can analyze the flow of incoming data and highlight the patterns or
abnormalities according to preconfigured parameters. This helps
greatly automate the analysis and provides powerful tools for finding
valuable information in the flow of unstructured data.
 Deep learning custom tools
Is a machine learning technique, which relies on learning data
representations, unlike task-specific machine learning algorithms. it

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definitely excels in analyzing unstructured data according to pre-
configured parameters and statistics, providing a great degree of
precision in evaluating the incoming data.
 Machine Learning
Machine learning is a branch of artificial intelligence that focuses on
allowing computers to learn new things without being explicitly
programmed. In other words, it analyzes existing big data stores to
come to conclusions which change how the application behaves.
 Data monetization
Predictive and prescriptive analytics are the approaches that help
organizations minimize expenses and earn money using their Big Data
tools. Selling the results of analytics as a service, disseminating and
structuring the data for customers can also become a profitable
market.
Is the act of generating measurable economic benefits from available
data sources. Typically these benefits accrue as revenue or expense
savings, but may also include market share or corporate market value
gains.
 R language and Jupiter notebooks
Data scientists have a number of option to analyze data using
statistical methods. One of the most convenient and powerful
methods is to use the free R programming language. R is one of the
best ways to create reproducible, high-quality analysis, since unlike a
spreadsheet, R scripts can be audited and re-run easily. The R
language and its package repositories provide a wide range of
statistical techniques, data manipulation and plotting, to the point
that if a technique exists, it is probably implemented in an R
package. R is almost as strong in its support for machine learning,
although it may not be the first choice for deep neural networks,

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which require higher-performance computing than R currently
delivers.
 Self-Service
As the cost of hiring big experts rises, many organizations are likely
to be looking for tools that allow regular business professionals to
meet their own big data analytics needs. IDC has previously
predicted "Visual data discovery tools will be growing 2.5 times
faster than rest of the business intelligence (BI) market.Several
vendors have already launched big data analytics tools with "self-
service" capabilities, and experts expect that trend to continue into
2018 and beyond.

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Applications of Big Data
1. Banking and Securities
In this industry include: securities fraud early warning, tick
analytics, card fraud detection, archival of audit trails, enterprise
credit risk reporting, trade visibility, customer data
transformation, social analytics for trading, IT operations
analytics, and IT policy compliance analytics, among others.

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2. Media and Entertainment
Big data is changing the media and entertainment industry, giving
users and viewers a much more personalized and enriched
experience. Big data is used for increasing revenues, understanding
real-time customer sentiment, increasing marketing effectiveness
and ratings and viewership.
3. Healthcare
Big data is used for analyzing data in the electronic medical record
(EMR) system with the goal of reducing costs and improving patient
care. This Data includes the unstructured data from physician notes,
pathology reports etc. Big Data and healthcare analytics have the
power to predict, prevent & cure diseases..
4. Energy and Utilities
In utility companies the use of big data also allows for better
asset and workforce management which is useful for
recognizing errors and correcting them as soon as possible
before complete failure is experienced.
5. Manufacturing
Increasing demand for natural resources including oil,
agricultural products, minerals, gas, metals, and so on has led to
an increase in the volume, complexity, and velocity of data that
is a challenge to handle.

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6. Education
In a different use case of the use of big data in education, it is
also used to measure teacher’s effectiveness to ensure a good
experience for both students and teachers. Teacher’s
performance can be fine-tuned and measured against student
numbers, subject matter, student demographics, student
aspirations, behavioral classification and several other variables
7. Transportation
 Governments use of big data: traffic control, route planning,
intelligent transport systems, congestion management (by
predicting traffic conditions)
 Private sector use of big data in transport: revenue
management, technological enhancements, logistics and for
competitive advantage (by consolidating shipments and
optimizing freight movement)
 Individual use of big data includes: route planning to save on
fuel and time, for travel arrangements in tourism etc.
8. Insurance
Big data has been used in the industry to provide customer
insights for transparent and simpler products, by analyzing and
predicting customer behavior through data derived from social
media, GPS-enabled devices and CCTV footage. The big data also
allows for better customer retention from insurance companies

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Big Data Security
Security Issues Comes due to:-
 There are limited levels of protection in majority of distributed
systems’ computations.
 Security solutions are not being able to tackle the demand with
several non-relational databases constantly evolving.
 There is lack of appropriate security processes for the transfer of
automated data.
 System updates, audits, patches are not always carried out.
 Information coming in should be constantly validated, to ensure its
credibility and accuracy, but that is not the case for most systems
 Attack on systems that contain sensitive and personal information
of the customers can put the customers at risk.
 Certain organizations do not deploy any kind of access controls to
differentiate between the confidentiality levels of data within the
organization.
 Monitoring and tracking of systems is difficult with the current
scale of Big Data application.
 Cyber Security Threats are Rising.

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How to secure the Big data
 Detection of frauds and replacement of SIEM (Security
Information and Event Management) systems
 Optimization of sales and marketing campaigns through business
intelligence as there is an abundance of data and analytics
available.
 Real-Time Security Monitoring
 Endpoint Input Validation and Filtering
 Secure Storage and Transaction Logging.
 Granular Access Control.
 Encrypted Data-Centric Security.
 Best Security Practices for Non-Relational Data Stores.
 Application Software Security
 Account Monitoring and Control

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End