In today’s digitized world, data has an important role to play in the growth of the business and IT sectors.
The information gained from data is used for understanding the patterns of consumer behavior and informing business decisions. The market trends we witness today are often a by-product of the insights derived from huge bulks of data.
Data science and its branches have been gaining popularity due to the scope of data in the international market. Data engineering and analytics are integral divisions of the data science field contributing to the extraction and analysis of data.
As much as they are parts of the data science sector, data analytics, and data engineering have different functions to perform in the context of data-oriented business decisions. Let’s look at the major points of difference between these two fields of data.
What is Data Analytics?
Every business sector, both national and international, is collecting customer data to conduct careful research and glean insights from it. Rather than depending on impulses, companies prefer basing their decisions on logical conclusions and hence are data-driven.
Data analytics involves the process of analyzing large amounts of data to derive useful information from it. These insights are used by organizations to plan their business strategies in the market.
Data analysts look for patterns in the derived data and draw inferences from it for the organization to market its business accordingly. Data visualization, data mining, data modeling, etc. are some of the functions of data analysts.
What is Data Engineering?
Data engineering is the practice of creating systems and models for collecting and organizing data for data analysts to interpret. According to IDC, the amount of global data will reach 163 zettabytes by 2025.
The large amounts of structured and unstructured data collected by the organizations should be stored and presented to the data analysts in such a way that it is understandable.
Data engineers, with the help of technology, design systems to feed these data so that it becomes accessible to others. So, the ultimate role of data engineers is to create data algorithms that transform raw unstructured data into useful information.
Differences Between Data Analytics and Data Engineering
Data analytics and engineering are the two sides of the same coin and are dependent on one another. However, the few differences between their areas of operations have been listed below:
- Data analytics involves analyzing the data to gain insights and data engineering is the process of building models to collect and prepare that readable data.
- Data analysts prepare graphs (data visualization) and reports from data to understand patterns of consumer behavior and data engineers are often involved in extracting unstructured data for analytical purposes.
- Data analysts may or may not have knowledge in coding and programming whereas data engineers are experts in handling complex programming languages like Python, SQL, etc.
Data analytics and engineering are identical branches of data science and help each other in filtering information from data to help organizations in developing marketing strategies.
If you would love to be a part of the data-driven business sectors, data analytics, and engineering are the fields for you with plenty of lucrative opportunities.
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