Data engineering has been around for a years only yet is confidently crafting its way to being essential for the most basic info operations in the future. Without data engineering, YouTube wouldn't have its recommended section that we all enjoy (or sometimes get weirded out by :) ), businesses wouldn't be able to target advertising to their most suitable audiences and the market wouldn't have such an accurate consumer behavior understanding to become as responsive to the consumer's needs as it is today. In other words, data engineering is changing the way we live in so many ways. But what is data engineering, exactly? And why it is so important for humanity? Let's start with the basics.

The Definition

Basically, data engineers deal with large data assets, which includes storing and processing data loads. More profoundly, the objective of data engineering is to build and maintain complex, scalable and well-functioning databases to keep loads of data accessible, protected and in good shape for further analysis. And the fact that every company has moved its data to digital databases makes data engineers responsible for the growth of almost all businesses existing today.

Data Engineering Is Everywhere

The need for a separate domain of data engineering occurred soon after people started to create data. Particularly, this is directly related to the Internet since it holds the majority of information ever existing: people started to upload stuff and post things massively, which now has to be managed, stored and looked after in some type of way. Some surprising statistics: Of course, seeing the 'usual' side of the Internet is routine for us, people. Scrolling Insta or listening to a playlist on YouTube while getting ready in the morning is something we're so used to. And all the content exists in the form of data loads that are stored somewhere out there, right? That's when data engineering comes to do the job: please welcome data science in advertising and media!

Where Did It All Start?

So here's the Internet; here are some useful websites, a couple of funny ones. And then there are news articles. And we can also shop online now. And watch movies. Or three in a row. Our access to info increases, yet we're still getting search results on whatever we need in less than a second. How does the Internet manage to filter the data SO quickly? With the evolving technologies, the need to manage all of this has occurred, and there were no other options for us besides figuring out a way to cope with data. And we did. Web scraping, crawling and data parsing are the answers humans have come up with to work with data. Making the most use of it by filtering to find what we need is how these approaches serve us. Data parsing, as the largest and widest methods of the three, is not that difficult to take up: a list of Python scripts help you do the job. The point is, a dozen of lines go through thousands of records at an incredible speed to give results that would take several lifetimes to receive manually. The difference between automated and manual lead generation methods illustrates this well. A manual lead generation would look for people that can be potentially interested in a product or service, put all the names/contacts in a list and send out emails to each of them, one by one. All this would take months! Instead, today’s lead generators can parse the info on the potential clients with a script to get the list of contacts and set email drip campaigns to reach out to all of them in just several clicks. Altogether, the simplest automated lead generation takes less than a week, riching more clients for less time. And you can think of the same comparison for a lot of jobs to see the importance and benefits of data engineering.

What We Already Can

Although the field of data engineering is on its initial stages of development, we’ve already achieved a lot in it. Let's take a look at what's called The AI Hierarchy of Needs:

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Data engineering is just on the second level of the hierarchy, the 'MOVE / STORE' level. It implies creating data pipelines and designing table schemes, to name a few. But skipping some of these core processes becomes a serious obstacle for businesses on the way to more complex data analytics operations and applications. One of the crucial aspects of data engineering is the ETL process, which stands for Extract, Transform and Load that describes its role pretty clear. 

Here's a visualization of the ETL process:

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 As you can see, ETL serves for simplifying work with data for humans: the approach implies collecting raw data from diverse sources and transforming it according to business logic, domains and other aspects. Eventually, the data loads into a ready-for-analytics form so that it can be used for whatever we need to.

The Future Is Now

So we learned how to deal with loads of data and use it for our good. While that's already a lot, there's so much more ahead. Now we see the world around us through the data, but soon we'll be able to foresee it. By advancing through a variety of data engineering and data science services, along with the levels of the AI hierarchy, we reach Big Data Analytics and AI. That's when the fun begins. Using data analytics services for data processing allows us to gain enough information to form patterns in literally any domain. Healthcare, banking, education, psychology - specialists in any industry and field will have info to predict future outcomes based on the existing principles defined with data analytics. Moreover, all these predicting processes will become automated and performed by technology that we already use. That’s why we need data engineering and how it will lead the future. Want to learn more about the opportunities for data engineering for your project? Data science is one of the Ralabs core industries, and our 50+ skilled engineers are ready to help you. Ask us expert advice on your project.