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:
- Data engineers are known for building and maintaining infrastructure and architecture for various databases generation. Whereas data scientists are mainly known for mathematical and statistical analysis on the generated databases. They are also someone who maintains big databases.
- Data engineers collect raw data from humanꓹ machines etc. the data are then contained with certain codes that are system-specific.
- Data engineers used a variety of tools and languages which work together and also improve efficiency and quality.
- They also used various specific codes for the information collected by the data scientists.
- The team for this data also ensures that all the work is done and process properly and also develops data set processes for data modeling and production.
Role of data scientists:
- A data scientist is usually used for sophisticated analytics programs and machine learning programs. They mainly deal with clients and conduct tasks with big markets and business operations for identifying certain trends.
- Data scientists research the industry from various internal and external sources to answer business needs.
- After the analysis is completed by the data scientist then they keep on check if the work is process so that they can deliver it to the business stake holders.
- The data scientist should be aware of distributed computing data which is the process by the data engineering team and also check and report a brief view on it to the business stakeholders which is important.
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.
