What is Data Engineering?

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Data engineering is an area that deals with the transformation of a company’s raw data.

This is the first stage of data processing, a series of activities that aim to do what we said before: to give practical use to a large amount of information available.

It all starts from the data collection, storage, and distribution processes, which are under the umbrella of data engineering.

Using a very basic example: imagine that you are organizing a wedding party and you have the guest list in hand.

The list is in no order, but next to each guest is information about their connection with the bride and groom (family, co-worker, neighbor, childhood friend, etc.).

The list information is raw, and a good way to take advantage of it would be to separate guests into groups – one for family, one for co-workers, and so on.

To do so is to transform raw data. But when we talk about data engineering, this work will be done by software and algorithms.

Therefore, these tasks involve a lot of technical knowledge – to design solutions from the databases – and strategic knowledge, to align the solutions with the company’s or customer’s objectives.

Thus, it is no exaggeration to say that, in the analogy with civil construction, the data engineering professional is, at the same time, an engineer and an architect.

What does a Data Engineer do?

A data engineer is a professional who, from the languages ​​of computer science, performs the tasks that we talked about in the previous topic.

More than these languages ​​– such as Java, Scala and Python, among others –, he must broadly master all the logic and complexity behind concepts such as big data and cloud computing.

Based on this knowledge, the data engineer designs builds and tests data processing systems architectures.

He will be responsible for data acquisition and data source blending solutions.

From there, the engineer creates the data pipeline, that is, the process through which the information passes, including entering the system, processing and storage, in order to facilitate later consultation.

It is important for the data engineer to also have knowledge of predictive and prescriptive analytics , to make the work that comes later as easy as possible (more on that later).

On the other hand, the more languages ​​he knows and the greater his specialization in them, the higher his salary in the market should be and the greater the job opportunities.

This means that data engineering is a field with a lot of room for growth for those who like to be constantly studying.

Data Science x Data Engineering

Another profession that has everything to do with today’s big data and business intelligence times is that of the data scientist.

What we talked about at the beginning of the text, about qualifying decision-making based on data, also has everything to do with this area.

For those who do not work in this field, data science is so similar to data engineering that the question arises: what is the difference between them?

The truth is that the two areas are complementary , and the scientist’s work is the one that comes after the engineer’s work.

While the data engineer develops an entire infrastructure to collect, organize and store the data, the data scientist uses his capabilities to treat it.

Based on knowledge in the area of ​​statistics and mathematics, in addition to programming and computer science, this professional generates valuable insights for his clients or for the company he works for.

The work of data science, therefore, is closer to the core activity of the organization, as it gives meaning, a purpose to the large amount of information that we talked about before.

Ideally, data scientists and data engineers work in tandem, and each one knows a little about the other’s area, in addition to the knowledge shared between the two.

And can the same professional perform both functions? Depending on the job demand, even it can.

But it is recommended that they are two separate positions, so that there is a greater degree of specialization possible so that each one only focuses on their objective.

A good manager needs to be open to all technologies. Understanding the importance of data is critical

Importance of Data Engineering for Administration and Marketing

A manager who is overly focused on his business may not immediately understand the importance of data science and engineering.

The problem lies precisely in your self-centered view. Not broadening the horizon and realizing the complexity of the current market poses great risks for the company’s future.

You have to understand that with the internet and globalization, there is more competition than ever before.

And consumer behavior changes at a much faster rate than ever before.

This scenario has even made traditional companies, some even centuries old, suffer from strong identity crises.

Because today, for a company to survive and prosper, it is not enough for it to have a well-known brand and experience in the market.

It needs to have the ability to adapt to the speed of change in today’s world.

To respond to this demand, the path is to undergo a digital transformation, which includes the implementation of agile methodologies and the use of technologies to qualify decision-making.

This is precisely what data engineering seeks to develop.

If we know that the market is volatile, we need to monitor and process its variables quickly, in order to develop the flexibility and agility that is required today.

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