What is big data? Big data is extremely large
or complex set of data and it is so large that it is difficult to process it using
traditional database and software techniques. Every day we are creating approximately
2.5 quintillion bytes of data. So where is this huge amount of data being generated?
from earlier we had mobile phones with the functionality of calling and text
messages or clicking some pictures maybe but with the new technologies like
smartphones we have a lot of applications for music, sports, social media like
Facebook, Twitter, LinkedIn, and many more also.
Data is being generated when we shop online. So
why does it need attention as the data is growing companies are capturing the data
that streams into their businesses. They can apply analytics and get significant
value from it with better speed and efficiency. Companies are leveraging the
benefits of big data by analyzing the patterns and trends and predicting
something useful out of it, for example, companies like Amazon and Netflix use
big data to improve the customer experience as we see here from the statistics
shown by 2020.
1.7 megabytes of data will be created every
second for each human. This needs immediate attention because this data cannot
be just thrown away it is going to give profit to the businesses big data challenges.
Big data is not just about the volume of data it poses. Other challenges as
well like velocity and variety as a volume 40 zettabytes of data will be
created by 2020. this huge volume of data is either human-generated like from
social media YouTube or candy machine-generated like through sensors and
personal health trackers and canal so be generated with organizations like card
details commercial transactions and medical records another challenge is the velocity
the speed at which data is coming into the system.
The data needs to be processed with faster
speed and then there is a variety of data is not only structured but also
unstructured and semi-structured data like images videos and tweets. so how our
enterprise is using this big data today. Let us see Big Data popular use cases
Internet of Things. These are numerous ways in which analytics can be applied
to the Internet of Things for example sensors are used to collect data. That can be
analyzed to achieve actionable insights tracking customer or product movement
etc. Many enterprises are creating a dashboard application that provides a
360-degree view of the customers that pulls data from a variety of sources
analyzes it and presents it to customer service.
Therefore, this allows them to gather rich
insights about businesses' big data. Popular use cases are related information
security and data where have optimizations big data tools are being used to
remove some of the burdens from the data warehouses. Even the healthcare
industry is looking for patterns and treatments that lead to the best outcomes
for patients. The main challenge of Big Data is storing and processing the data
at a specified time span. The traditional approach is not efficient in doing
that. Therefore, Hadoop technology and various big data tools have emerged to
solve the challenges faced in the Big Data environment. Therefore, there are many
big data tools and all of them help the user in some or another way in saving
time money and uncovering business insights. These can be divided into the
following categories like data storage and management.
The next broad category is data cleaning. Data needs
to be cleaned up and well structured. Examples of such tools, which help in
defining and reshaping the data into useable data sets, are Microsoft Excel. Data
mining is a process of discovery in sites within a database some of the popular
tools used for data mining are Terra data and rapid miner. Data visualization
tools are a useful way of conveying and complex data insights in a pictorial
way that is easy to understand. For example, Tableau and IBM Watson analytics
and Plotly are the common tools for data reporting. Data ingestion is the
process of getting the data into Hadoop, which can be done using scooped flume
or storm data analysis requires asking questions and finding the answers and data.
The popular tools used for data analysis are Hive,
Pig, MapReduce, and Spark. Data acquisition is also used for acquiring the data
for which scoop flume or storm tools are quite popular. The popular Big Data
tools offer many advantages, which can be summarized; as follows, they provide
the analyst with advanced analytics algorithms and models. They help the user to
run on big data platforms such as Hadoop or any high-performance analytic
systems. They help the user to work with not only structured data but also
unstructured and semi-structured data coming from multiple sources. It is quite
easy to visualize and analyze data in a form that helps in conveying the
complex data insights in a pictorial way, which is easy to understand by users big
data tools help you to integrate with other technologies very easily.
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