What is data engineering and is it right for you?

What do data engineers do?

Data engineering is a very broad discipline that everyone calls it differently. Very often, the data engineer is not named at all. This is probably why it is better to first define the goals of data engineering and then discuss which work is producing the desired results. The ultimate goal of data engineering is to provide an organized, consistent data flow for the work involved in using it, for example:

Exploratory data analysis;

Generation of external data for automation applications.

There are many ways to do this, specific sets of tools and techniques, and the skills required vary widely depending on the team, organization, and desired outcomes. However, the data pipeline has become the most common data processing pattern. It is a system composed of independent programs that perform various operations on input or collected data.

Data pipelines are often distributed across multiple servers: what does an engineer do

Data pipeline

This diagram shows a simplified example of a data pipeline that provides the most basic architecture you may encounter. Below you will see a more complex view.

Data can come from any source:

Devices Internet of Things

Vehicle telemetry.

Real estate data feeds.

Normal user activity in the web application.

Any other collection or measurement tools you know ...

Depending on the nature of these sources, the input data will be processed in streams and in real time, or with some regularity in packets .

The data engineer is in charge of the pipeline through which the data flows. Data engineering teams are responsible for the design, construction, maintenance, expansion, and often the infrastructure that supports the data pipeline. They can also be responsible for the input data, or more often the data model and how that data is ultimately stored.

If you think of the data pipeline as an application, then data engineering begins to resemble any other discipline of software engineering.

Many teams today are moving towards building data platforms . For many organizations, it is not enough to have just one pipeline storing incoming data somewhere in a SQL database. Large organizations have multiple teams that need different levels of access to different types of data.

For example, artificial intelligence (AI) teams may need ways to tag and split cleaned data. Groups of business intelligence (BI) may require easy access for aggregating data and building data visualization. Data analysis teams may need a database — the access layer to properly explore the data.

If you are familiar with web development, you may find this structure similar to the Model-View-Controller (MVC) design pattern or Model-View-Controller. In MVC, data engineers are responsible for the model, AI or BI teams work on views, and all teams interact with the controller. Building data platforms that cater to all of these needs is becoming a top priority in multi-team organizations that rely on data access and use for their businesses.

Now that you've learned a thing or two about what data engineers do and how important they are, it might be helpful to learn a little more about their customers and the responsibilities data engineers have to them.