Since Harvard Business Review declared the Data Scientist Job as the “Sexiest Job of the 21st Century” back in 2011 – 2012, everyone wants to be a data scientist. But, at present, data engineers are in greater demand than data scientists. So, who are data engineers and how they are different from data scientists? In this article, we will try to decode the basic differences between data engineers and data scientists.
As per Gartner, only 15% of Big Data projects ever make it into production. According to the article by Todd Goldman, one of the key reasons behind the failures is to build a production pipeline, which is one of the primary tasks of a data engineer. Data scientists get much attention in today’s age of analytics. But, equally important are the roles of data engineers. Data science and data analytics cannot prosper if there is no data engineering workbench.
Even according to Glassdoor, the number of job openings for data engineers is almost five times higher than the number of job openings for data scientists. However, as per Editor’s comment on KDnuggets, as of May 14, 2018, there are 2,500 data engineer jobs in the US as compared to 4,300 data scientist jobs. This could be due to the fact that several organizations don’t always (or may be unable to) draw the line between a data engineer & data scientist. Anyway, it’s fact there are major significant differences between a data engineer and a data scientist. Several reports have also demonstrated that the majority of organizations need more data engineers than data scientists on their team. Now, we will move on to understand what is data engineering and data engineer vs data scientist.
Data engineering includes what some companies might call Data Infrastructure or Data Architecture. Data engineers create the process stack for collecting or generating, storing, enriching and processing data in real-time or in batches and serves the data via a middleware for further analysis by other disciplines.
Data engineering usually employs tools and programming languages to build API’s for large-scale data processing and query optimization. Specialists who deal with data engineering are also known as Big Data Engineers or Big Data Architects.
At the core, data engineers possess a programming background (Java, Scala or Python). In contrast, data scientists are usually from Math, Statistics, Economics, or Physics background.
A data engineer develops, constructs, tests and maintains architectures, such as databases and large-scale processing systems.
A data scientist, on the other hand, is someone who cleans, massages, and organizes (big) data.
