What is Data Engineering?

Data engineering is arguably one of the most important and in-demand fields in the data science jobs industry today. According to Indeed, the number of job postings for data engineers has increased by more than 400 percent in the past five years.

But what is data engineering and why is it gaining so much importance?

Recent years have seen a huge rise in the quantity of data that is generated by individuals and companies and the use and application of that data. We have seen an increase in the value of data and organizations are now paying more attention to the data they generate and how they can use it to their advantage. This has led to a corresponding increase in data roles and applications, especially in fields like data science and Machine learning.

With this rise in data generation and utility comes the need to devise efficient and capable methods of obtaining data, processing it and making it available to end users. If there is one thing that industries trying to derive the most utility out of their data have realized in the past couple of years, it is that it is almost impossible to do meaningful work without quality data.

The demand for quality data

While Data is extremely important, it is not any kind of data that is useful. Data comes in different types and formats. To get the most out of one's data, it needs to be the right data, in the right state and format. Companies need to collect data that are relevant to them, store them, and process them to ensure that it is usable for them. Companies need to determine the type of data they need, how to collect them and how to store this data. Data analytics, Data science are all well and very good, but organisations have realized that analytics dashboards and machine learning models can only be as good as the underlying data. In fact, the biggest challenges facing most machine learning projects are inadequate or poor-quality data. This has led these organizations to pay more attention to their data infrastructure.

This is where data engineers come in. They design infrastructures and systems such as databases to collect and optimize data to ensure that the data is available and accessible in the right format for all other users of data in the pipeline. They lay the groundwork that makes all other data activities possible. Initially, some of these roles were carried out by roles such as database administrators, data warehouse engineers and even software engineers but as the volume increased, it became imperative to have people and systems dedicated to handling this new type of data, as traditional data management tools were quite simply not capable of dealing with the emerging demands.

Today the job of a data engineer is a combination of a lot of fields; software engineering, DevOps, cloud computing etc.

The best definition of data engineering I have come across so far is by Joe Reis and Matt Housely in Fundamentals of Data Engineering where they described Data Engineering as follows: The development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning. Data engineering is the intersection of data management, DataOps, data architecture, orchestration, and software engineering.

In my next series, I will be discussing tasks and technologies used by data engineers.