Data Engineer vs Data Scientist: Which one is better?
In today’s world Data science is known as the potential way of success in every field and industry.[According to python blog] Various opportunities regarding jobs and their roles and responsibility can be seen all around the world. Data engineers are advanced having problems solving techniques and building. Both together with data scientists and data analysts they work together for transforming raw data into a competitive way.
Soꓹ today we are going to discuss some of the differences between data engineers and data scientist and their roles in it.
Role of data engineers:
Role of data scientists:
Languages and tools for data engineers and data scientists:
These include both commercial and various sources of a factor in it.
Data engineers work with tools like SAPꓹ mySQLꓹ redisꓹ oracleꓹ hive etc whereas Data scientists required to have a good understanding of programming languages such as SASꓹ SPSSꓹ Rꓹ pythonꓹ stata and Julia for building models.
Python and R are mainly known as the most popular tool but working with them usually comes with various packages for great data visualization in it. Some of the packages like NumPyꓹ sci-kit-learn etc are used for working on a data project.
Some of the difference in the languages is that Scala is mostly used by data engineers for setting up large extra transform load (ETI) flows and java is mostly popular with data scientists.
Salaries and job:
A data scientist mostly earns 135000 on a yearly basis and a data engineer earns mostly 126000 on a yearly basis. Both salaries mainly depend on their work and their open positions. There are mostly about 85000 jobs available for data engineers and 111000 jobs for data scientists on the markets.
Various companies like play stationꓹ bloomvergꓹ The New York times are hiring data engineers and companies like Microsoft and Walmart are open for data scientists.
But due to the rise of problems in management for data storage companies started looking for cheaper and easier solutions and started moving data to the cloud.
These day companies used to compose data instead of hiring unicorn data scientist which had good communication skillsꓹ creativity and expertise in various techniques etc which are hard to find in a person that carry all the qualities which the companies demand.