The functions of Data Scientist and Data Engineer are similar in many ways, so sometimes these specialties are considered interchangeable.
Perhaps, at the stage of the birth of big data, this was the case, and the boundaries were not yet clearly separated. But now, it is a separate large area with many of its tasks, tools, and approaches. One specialist can no longer absorb all the necessary skills, so the roles of a data scientist and a data engineer are often separated.
We will tell you the difference between a data engineer and a data scientist, what tasks each specialist performs, and what tools he uses. First of all, the article will be useful to those who plan to deal with big data and want to choose: Data Engineer or Data Scientist.
What A Data Engineer Should Know And What He Does
The area of responsibility of a data engineer is to build a reliable and efficient architecture for working with data. It sets up and maintains data processing systems, creates pipelines for loading data from various sources, and cleans and filters incorrect information.
A data engineer needs to know how different parts of the architecture interact with each other; he must be able to integrate them and build a complete process from collecting raw data to bringing the analysis results to the customer. At the same time, the data engineer must understand the capabilities of the technologies used and their limitations. Let’s explain with an example.
A manufacturing company collects data from several sources: logs from IoT sensors, files, videos, tables, etc. The process of collecting, storing, and analyzing the entire volume of this data must be organized so that all interested departments can quickly receive the information they need. Establishing and supporting this process is the task of a data engineer.
Suppose a company wants to collect and store more data to make decisions based on it. In that case, the job of a data engineer is to understand whether the existing architecture is suitable for these tasks or if something needs to be changed: add capacity, integrate new tools, or even completely rebuild the architecture.
Another aspect of the data engineering profession is data preparation and cleansing. Data engineers work with raw (raw) data, which may be incomplete, contain errors, or not suitable for solving the problem. They prepare data for further processing: they automate the collection, cleaning, and transformation into a form suitable for analysis.
Although the data engineer is responsible for working with data, he looks at them in structure, storage logic, and processing efficiency. He is responsible for ensuring that the data is suitable for further processing. The data engineer also ensures the availability of data: a ready-made architecture should allow you to access data and receive a response to a request quickly. At the same time, he does not analyze business information in this data and does not try to look for patterns and insights.
Also Read : What Can A Data Scientist Do?