DATA ANALYTICS DATA ENGINEERING

Today's organizations generate a huge amount of data that needs to be optimized and transformed into useful business knowledge. Our team of qualified and experienced Data Engineers and Consultants will create high-performance infrastructure and optimize your data to help you make better decisions and achieve your business goals.
Data has always been vital to any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. In this article, we will discuss the key differences and similarities between a data analyst, data engineer and data scientist.
For many employers’ data engineers, data scientists, and data analysts appear to be different names for the same role. In reality, these roles span a variety of different skill sets and responsibilities, although all of them deal with data sets and play a key role in refining data strategies.
Data engineers build, test and maintain data ecosystems. These ecosystems are essential for companies, and data scientists in particular, whose job is to analyse data in order to build prediction algorithms. As such, we can say that what data engineers do is instrumental to data scientists.
Data analysts create ad-hoc and regular reports based on past and current data in order to find answers to business questions. This role is often seen as the stomping ground for someone interested in a data-related career.
The difference between data analyst and data scientist roles is that the scope of work of data analysts is limited to numeric data, whereas data scientists work with complex data. In this article, we have compared these three roles to provide a comprehensive answer basing on our experience and Internet resources on this topic.