What's the difference between Big Data and Data Science?
Two important ideas in the realm of data are big data and data science,
however, they differ significantly. We'll attempt to explain these disparities
in this post and how these two fields complement and depend on one
another.
Characteristics of Big Data :
Big data, usually called "mega data," is a general term that refers to the development, gathering, management, and processing of extremely large quantities of data. Terabytes, petabytes, and even exabytes are used to describe this data.
Big Data is distinguished by both its enormous
volume and its velocity, or the rate at which new data is produced. Consider
the yearly billions of searches conducted on Google, the films posted on
YouTube, the images shared on Instagram, or the purchases made at retail
behemoths like Walmart or Amazon.
The fact that Big Data is frequently heterogeneous means that it can be either structured (in the form of neatly arranged tabular data) or unstructured (text, photos, videos, etc.). To store and process this enormous amount of data in a distributed fashion, big data management requires specialized technologies like MapReduce, Apache Hadoop, and Spark. Big Data specialists who manage this intricate infrastructure include data engineers, data architects, and developers.
Data Science exploits data:
Data Science is a discipline that falls under the Big Data umbrella but
focuses more on using data to address particular business issues. Although they
are active upstream and downstream of the big data ecosystem, data
scientists do not directly control the big data infrastructure.
To address business issues, analyze data, run experiments, and make
predictions, data scientists specify the data requirements upstream. The data
engineers who gather, store, transform, and process the data in the Big Data
ecosystem use these criteria as crucial inputs.
After the data is available, data scientists utilize downstream
exploration, analysis, and prediction tools to wring value out of the data and
address business issues. Data scientists can use the wealth of data provided by
Big Data to mine it for information that is hidden and to add value to the
enterprise.
Data Scientists discover data needs, Data Engineers gather them and make
them available, and then Data Scientists use this data to create value, which
might lead to the identification of new data needs. This interaction between
Big Data and Data Science can be viewed as a positive feedback loop.
Even though managing Big Data isn't the core of a Data Scientist's job,
it's still important for them to be familiar with the technologies and
techniques that are employed in this field. With the help of this
understanding, communication with big data specialists is made easier, which
improves the definition of needs and requirements.
In other words, while Data Science focuses on using this data to solve
business challenges, Big Data is concerned with handling huge amounts of data.
These two professions are related, and a working knowledge of big data can help
data scientists collaborate more effectively inside an organization's data
ecosystem.