Do you Know the Difference Between Data Engineering vs Data Science?
Belonging to the world of technology involves hearing many concepts that may sound similar to each other. And one of them may be data engineering vs. data science. Although they share some similarities, the reality is that there are many important differences between them.
For this reason, the purpose of this article is to inform you and let you know what each concept means. Read on and find out more about the difference between data engineering vs data science!
Data engineering vs Data Science: what are the similarities and differences between the two terms?
Well, to learn more about data engineering vs. data science, it is necessary to know that in the world of technology and data there are many professions and roles. Precisely, this is the main shared characteristic between both concepts: both the engineer and the data scientist are constantly working with large volumes of Big Data.
However, the difference is in the purpose. Engineers are in charge of extracting large volumes of information and organizing databases. On the other hand, data scientists perform visualization tasks, diagramming learning tasks, and patterns on the data previously extracted by engineers.
For this reason, the tools used by each tend to vary. In the case of data scientists, they usually use resources such as Deep Learning, Machine Learning, data processors (such as Spark), or programming tools such as R or Python. In this way, engineers use databases such as SQL and NoSQL, the Hadoop ecosystem, and tools such as Apache Airflow or Dagster.
It should be made clear that both are indispensable professions for any company that wants to take advantage of technology. However, this serves only as an introduction to the subject. For this reason, we recommend that you read on to find out more about each of these fields of work.
What does data engineering consist of?
Let’s specify a little bit the roles that are practiced in data engineering. According to Coursera, it is the practice of designing and building systems that collect and store large volumes of data. Therefore, the engineer is the person who is responsible for building and maintaining data structures for use in multiple applications.
The ultimate goal of the data engineer is to make all this data accessible for the organization to consider in decision-making. In other words, the idea is that this data is transformed into useful information that executives can use to maximize profits and see growth in the company.
It is for this reason that a data engineer must have advanced knowledge of databases. Likewise, as there is a trend towards cloud programming, he or she needs to know all these systems. This professional must also be able to work in conjunction with different departments, to understand the organization’s objectives.
So, it is key to understand that data engineers will not only need to be passionate about programming. They will also need to have communication skills, as they will be working in conjunction with other departments and professionals, as is the case with data scientists.
And what specifically is Data Science?
Now, you may want to know more details about data scientists, which is another of the most sought-after professions by companies in recent years. IBM considers that data science combines knowledge in mathematics, statistics, programming, and artificial intelligence, to make efficient decisions and improve the company’s strategic planning.
It should be noted that Data Science is not synonymous with Artificial Intelligence. In reality, a data scientist uses Artificial Intelligence to extract useful information from unstructured data. AI is a series of algorithms that mimic human intelligence to read and understand data, but it is the scientist who makes the final decision.
This situation means that the data scientist has to be a person with a strong sense of logic. Not only will they have to work by studying the behavior of the data, but they will have to understand what the company wants. For this reason, they must not only master statistical software and programming but also have a strong interest in market and company situations.
Similarly, it should be considered that the data scientist will not only obtain data from a single source, as a traditional data analyst would do. Here they will seek to have a global perspective of the problem. Although they will bring their subjectivity to include their point of view in the decision-making process, the objective data will reinforce their arguments.
In short, you have seen that understanding the difference between data engineering vs data science is not complicated at all. Both professions are essential to working with Big Data since taking advantage of large volumes of information is key to achieving great results in a company. We hope this article has cleared up your doubts!