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Data engineers and Data scientists have been two sides of the same coin, both as important as the other in the greater scheme of things since a long time. The future of Data Analytics is nothing short of exciting. Initially, the focus used to be only on the collection and visualization of Data. Now that these basic things have been taken care of, there is emphasis laid on how to track, transform and manage data better. So in a way the next phase is all about redefining of organizational goals, in order to achieve accessibility, flexibility and efficiency in the new Data engineers being introduced to the industry.

Another important question arising here is whether the value of the data scientists is going to be undermined by data engineers in the future?

It cannot be denied that Data engineering as a concept holds more importance than Data science. It includes analyzing data and building models through maintaining the infrastructure for data scientists.

Data Engineering can be said to be the foundation of a solid data-driven organization. It helps in the facilitation of the data process development in order to store, clean, accumulate and process data, whether in real time or in smaller batches as well as prepare it for further analysis. Creating support systems for data is the primary function of Data engineers. In the larger sense, they are a part of the same team, like two co-dependant parts of a well-oiled machine.

While statistics, linear algebra, machine learning and computer programming are the fundamentals of data science, data pipelines, Big data storage, processing and ETL model are the fundamentals of data engineering.

Who is more important? Data Scientist or Data Engineer?

While the major focus of data engineers is to develop the architecture and infrastructure for data generation, Data scientists are responsible for statistical analysis, advanced mathematics and so on. While the roles of a data engineer and a data scientist are distinct, there are some overlaps. The job of the engineer could be tricky, based on the data that it is built upon.

In terms of languages, Python and SQL are a must know for both the jobs, but companies that are looking for highly skilled data professionals are more likely to go with candidates that have a skill set of data modelling, data warehousing, big data tools, data warehousing and so on.

In either case, Data engineers alongside data analysts that work for transformation of raw data offering a competitive edge to the enterprises. In a sense the work of data scientists depends on data engineers.

Data Engineers are more familiar with NoSQL, MySQL, Cassandra and SQL whereas Data analysts focus more on statistical modelling, Hadoop and SPSS. It however cannot be denied that both the skill-sets are of vital importance to the success of any organization.