We live in the age of data revolution, where everything that surrounds us is related to a data source and everything in our lives is digitally captured. There are not so many people in the world that hasn’t heard about Big Data. We read about it in newspapers, celebrities speak about it. Elon Musk worries about the way AI development might affect humanity, while Bill Gates thinks that data will make our lives easier.

Machine learning finds its implementation in various fields of human life, from the automobile industry with its self-driving cars and healthcare like tracking every patient's heartbeat over a lifetime to predicting the behavior of consumers from millions of store purchases or the typical email anti-spam protection. AI and ML have been deemed a driving force for business by more than 80% of chief executives of the companies numbered among the 500 largest in the world. Data science is already changing our lives, but data engineering remains in the background.

The hype around the topic “AI will enslave humanity” has recently been growing, but so far none of these fears have been justified. On the contrary, data analytic engineers are developing the technologies needed for the successful operation of data science applications.

At this point, it’s time to mention about data engineering.

1. Data Science Is Impossible Without Data Engineering

Data Science Is Impossible Without Data Engineering

Without data engineering, there would be no data as such, which would bring machine learning and AI to an end, because these technologies use algorithms that are requiring a lot of data to build.

2. Data Engineering Provides Data Transmission Speed

Data Engineering Provides Data Transmission Speed

It is not enough just to have data; it also should be comprehensive and needs to be constantly updated. That’s why DE for companies is very important. Outdated data predicts many things - such as customer outflow, fraud, etc.- pointless. If fraud schemes for stealing money from credit cards are detected a few weeks later instead of immediately, the credibility of the bank will be seriously shaken.

3. Increase In Data Volume Improves Forecasting

Increase In Data Volume Improves Forecasting

Big data is called so precisely because the bigger the data, the more accuracy there is in forecasting. Lack of data and the ability to manage it impedes many entrepreneurs. Yet even the world's largest companies have no way to deliver the necessary data for AI and machine learning quickly and without loss, but right now they are working together with data engineers on creating a well-organized data pipeline.

Not so long ago Big Data world was very expensive and difficult to scale. If your data center ran out of storage, you had to buy new expensive devices, install and configure them. It took months, the speed of data processing and its amount accumulated very slowly. Now, according to full-stack data engineers, companies can increase their data processing ability tenfold in minutes by just plugging it into a new cloud-based service. We need data like water, and to fill our reservoirs we need data engineering companies to build the “pipes”. These “pipes” are also needed to transfer the data from their old storage and applications to the places where they can be used. Those who ignore the need to harness their data wealth effectively may soon be left with nothing.

Title photo by: unsplash-logoCaleb Ralston