ASSIGNMENT:1

                                       TOPIC :
BIG DATA

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A proper definition
of “big data” is difficult to achieve because projects, vendors, developers,
and business professionals use it quite differently. With these things in mind,
generally speaking, big data is:

Bunch
of large datasets
Or a
category of computing strategies and technologies that are used to handle
large datasets

Where “large dataset”
means that a dataset too large to reasonably process on a single computer or
store with traditional tooling. This means that the common scale of big
datasets is constantly shifting and may vary significantly from organization to
organization.

Technology has taken over every field
today resulting in huge data growth. All of this data is valuable. 3 to 4
million data is used every day. One machine can’t store and process this huge
amount of data therefore the need to understand big data and methods to store
this data arises. Big data is a huge
amount of data which can’t be processed using traditional systems of approach (computer
system) in a given time frame.  

Here, big data is used to
better understand customers and their behaviors and preferences. Companies are
keen to expand their traditional data sets with social media data, browser logs
as well as text analytics and sensor data to get a more
complete picture of their customers.

Big Data Sources. Big data sources are
repositories of large volumes of data. … This brings more
information to users’ applications without requiring that the data be
held in a single repository or cloud vendor proprietary data store.
Examples of big data sources are Amazon Redshift, HP Vertica,
and MongoDB.

The general consensus of the
day is that there are specific attributes that define big data. In most big
data circles, these are called the four V’s: volume, variety,velocity,
and veracity. (You might consider a fifth V, value.)

That’s why big data analytics
technology is so important to heath care. By analyzing large
amounts of information – both structured and unstructured – quickly, health
care providers can provide lifesaving diagnoses or treatment options almost
immediately.

Now how big
does this data need to be? There’s a common misconception while referring the
word big
data. There’s not a threshold of data above which data will be
considered as big data. It is referred to data that is
either in gigabytes, terabytes, petabytes, exabytes or size even larger than
this. This definition is wrong. Big data depends purely on the context it is
being used in. Even a small amount of data can be referred to as big data. For
example, you can’t attach a file to an email with a size of 100 MB. Therefore
for the email, this 100 MB is referred to as big data.

Big data tools: Talend Open Studio. Talend also offers an
Eclipse-based IDE for stringing together data processing jobs
with Hadoop. Its tools are designed to help with data integration, data quality,
and data management, all with subroutines tuned to these jobs.

So, ‘Big Data’ is also a data but with a huge size. ‘Big Data’ is
a term used to describe collection of data that is huge in size and yet growing
exponentially with time.In short, such
a data is so large and complex that none of the traditional data management
tools are able to store it or process it efficiently.

Statistic shows that 500+terabytes of new
data gets ingested into the databases of social media site Facebook, every day.
This data is mainly generated in terms of photo and video uploads, message
exchanges, putting comments etc.

Benefits of Using
Big Data Analytics

·        
Identifying the root causes of failures and issues in real time.

·        
Fully understanding the potential of data-driven marketing.

·        
Generating customer offers based on their buying habits.

·        
Improving customer engagement and increasing customer loyalty.

·        
Reevaluating risk portfolios quickly.