What is data engineering?

We hear more and more about data engineering. And for good reason, this discipline is now a branch in its own right of the data sciences. Data engineering concentrates on the design and structuring of data flows in order to enable optimal exploitation. This step in the data processing process is crucial in view of the increasing number of data flows and the quantity of data.


What is data engineering?

Data engineering is a discipline aimed at organising, structuring, and selecting data in such a way as to allow adequate processing. The objective of data engineering is to select, sort and arrange data in such a way that its quality and relevance can be guaranteed. Data engineering is therefore an essential complement to data sciences. The two disciplines that used to merge are now distinct from each other.

Gartner, a leading consulting firm, defines data engineering as follows: "data engineering is the discipline of making suitable data accessible and available to different types of data consumers (including data scientists, business analysts, data analysts and others). »

The popularity of the discipline is growing, and the numbers don't lie. The demand for data engineers is exploding, growing at more than 30% per year. While a few years ago, the data scientist was shining in the spotlight, today it is the data engineer that companies are turning a blind eye to.

What is the purpose of data engineering?

Without data engineering, companies risk rapidly suffocating under the weight of useless data. Remember the expression "finding a needle in a haystack"? It is a perfect illustration of one of the primary functions of data engineering. The objective of the data engineer is to identify, access and use relevant data.

The very basis of data engineering is therefore the creation of data pipelines. Like other kinds of engineers, data engineers design and build structures. Data engineering must allow for scalability as well as optimal security.

Another aspect of data engineering includes the production of data science models. In recent years, many tools have emerged to facilitate this aspect of the work. This is notably the case of the Ryax platform; we will come back to this later.


Origin of data engineering

The discipline is not new. The premises of data engineering were already found in the 1980s. Some even trace the origins of data engineering back to the 1950s.

However, it was in the 2000s that the need to structure data became unavoidable with the arrival of Big Data. Nevertheless, the name only became widespread much later, in the early 2010's. Companies such as Facebook or Airbnb that were sitting on a pile of data started talking about data engineering.

In terms of function, the amalgam between data scientist and data engineer existed for a long time. Nowadays, the role of the data engineer has grown, and data engineering is recognised as a discipline in its own right.

Why is data engineering essential?

In recent years, data has multiplied at lightning speed. Companies that once struggled to collect data now need to sort it out. In order to make the right decisions, the right data must be used. This is the essence of the well-known expression in the industry: "Garbage in, garbage out".

The role of data engineering is therefore mainly at the level of ETL (Extract Transform Load) processes and database structuring (e.g. creation of data lakes). We can distinguish different main lines of work:

  • Collecting data from different sources (ETL). The data engineer works with existing software but can also develop his own tools;
  • Structuring the data;
  • Identifying and eliminating erroneous or irrelevant data; or
  • Standardize the data so that they can be processed.

This organisational work is essential. In fact, statistics concerning the percentage of data science projects coming into production are widely known. Deborah Leff, Chief Technical Officer Data Science and Artificial Intelligence at IBM estimated this figure at 87% in 2019. According to her, one of the major reasons for this low success rate would be that the data exists in different forms, in different units with different security or confidentiality protocols. The data therefore needs to be collected and cleaned for use. This is exactly where data engineering comes in.

Moreover, data engineering is crucial for the development of machine learning and artificial intelligence. Indeed, to ensure proper operation, the quality of data, especially training data, makes a real difference. This is where data engineering comes into its own.

Ryax and data engineering

A large part of data engineering lies in the creation of software adapted to the company's needs. It has been said that the role of data engineering has become more complex in recent years as a result of developments in the fields of Machine Learning and Artificial Intelligence.

In order to facilitate the process of data analysis and production start-up, Ryax start-up has developed a data processing platform. This platform takes the form of an on-demand software package, SaaS (Software as a Service).

Ryax is therefore a data engineering platform that helps to put data science models into production. Ryax automates part of the data engineering function in order to allow teams to focus on more essential elements such as the implementation of a solid, secure, and scalable data architecture.

Our intuitive platform enables optimal collaboration and communication. To understand the benefits of our product, please have a look at our use case examples. If you would like to know more, Ryax is at your disposal.

The Ryax Team.