BIG DATA
BIG DATA
What
is Big Data?
Big data is larger,
more complex data sets, especially from new data sources. These data sets are
so voluminous that traditional data processing software can’t manage them.
But these massive volumes of data can be used to address business problems you
wouldn’t have been able to tackle before.
The History of big data
Although the concept of big
data itself is relatively new, the origins of large data sets go back to the
1960s and ‘70s when the world of data was just getting started with the first
data centers and the development of the relational database.
Around 2005, people began to
realize just how much data users generated through Facebook, YouTube, and other
online services. Hadoop (an open-source framework explicitly created to store
and analyze big data sets) was developed that same year. NoSQL also began to
gain popularity during this time.
The development of
open-source frameworks, such as Hadoop (and more recently, Spark) was essential
for the growth of big data because they make big data easier to work with and
cheaper to store. In the years since then, the volume of big data has
skyrocketed. Users are still generating huge amounts of data—but it’s not just
humans who are doing it.
With the
advent of the Internet of Things (IoT), more objects and devices are connected
to the Internet, gathering data on customer usage patterns and product
performance. The emergence of machine learning has produced still more data.
While big
data has come far, its usefulness is only just beginning. Cloud computing has
expanded big data possibilities even further. The cloud offers truly elastic
scalability, where developers can simply spin up ad hoc clusters to test a
subset of data. And graph databases are
becoming increasingly important as well, with their ability to display massive
amounts of data in a way that makes analytics fast and comprehensive.
How does big data work?
Big data gives you new
insights that open up new opportunities and business models. Getting started
involves three key actions:
1.
Integrate
Big data brings together data from many disparate sources and applications.
Traditional data integration mechanisms, such as extract, transform, and load
(ETL), aren’t up to the task. It requires new strategies and technologies to
analyze big data sets at terabyte, or even petabyte, scale.
During integration, you need
to bring in the data, process it, and ensure it’s formatted and available in a
form your business analysts can get started with.
2.
Manage
Big data requires storage. Your storage solution can be in the cloud, on-premises,
or both. You can store your data in any form you want and bring your desired
processing requirements and necessary process engines to those data sets on an
on-demand basis. Many people choose their storage solution according to where their
data resides. The cloud is gradually gaining popularity because
it supports your current computing requirements and allows you to spin up
resources.
3. Analyze
Your investment in big data pays off when you analyze and act on your data. Get
new clarity with a visual analysis of your varied data sets. Explore the data
further to make new discoveries. Share your findings with others. Build data
models with machine learning and artificial intelligence. Put your data to
work.
Conclusion
Big data has immense
potential to revolutionize industries, drive innovation, and improve
decision-making. Its vast and complex datasets offer valuable insights and
opportunities for growth. Harnessing its power requires advanced analytics and
data management strategies to unlock its full potential for organizations in
the digital age.
Reference
Hitansh Lakkad
Business Analytics student at the International School of Management
Business Analytics
intern at Hunnarvi Technologies Pvt Ltd in collaboration with nanobi analytics.
VIEWS ARE PERSONAL
#Bigdata#analytics#datastorage#datamanagement#datastorage
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