It’s no secret that data is driving many of the technologies we use in our daily lives. Whether it’s tracking our fitness through a smartwatch, or streaming movies on one of the many streaming services available, data is integral in making our lives easier and more convenient.
And don’t forget about the role it plays in business, where an increasing number of businesses are using machine learning algorithms and advanced analytics to improve their marketing, sales, and operations processes.
There is, however, a significant problem. Nowadays, huge amounts of data come from a variety of sources in different formats and at various times. So, before data can be analyzed and be ready to use by end-users or data scientists, it must be transformed into a uniform set of data that is highly usable.
That’s where data engineering comes in. But what exactly is data engineering and why is it so important? In this post, we’ll look at these questions in more detail.
What is Data Engineering?
At its core, data engineering is the process of designing and building pipelines that transform and transport data into a form where data scientists or end-users can use it. In other words, data engineering involves collecting, generating, storing, enriching, and processing by designing and building data infrastructure and data architecture.
This infrastructure or data pipeline then gathers data from a variety of sources and collects it into a single, central hub in a uniform format, where it can be analyzed, and insights gained from it. As such, it often involves aspects of software engineering, databases, and extract, transform, and load (ETL) processes.
The key takeaway is that, while data scientists use data to obtain actionable valuable insights from it, data engineering is responsible for the underlying infrastructure and architecture.
Why Is Data Engineering So Important?
Over the last decade, an increasing number of companies have strengthened their digital transformation efforts. Although this has been slow going, these efforts accelerated significantly during the COVID-19 pandemic.
As a result, many of these companies are employing more digital platforms and services in their businesses. Also, the advent and popularity of big data have made this possible for businesses to collect more data than ever before.
The problem is that many businesses don’t have the necessary infrastructure or architecture to deal with this amount of data. In fact, as far back as 2017, Gartner stated that 85% of big data projects fail. According to them, this was due to a lack of reliable data infrastructure. In simple terms, businesses could not trust the data to place their business decisions on.
And the situation has not improved, with a vast majority of data science projects still failing.
As a result, data engineering is crucial to ensure the data’s quality, stability, and security to enable data scientists to do their jobs. This is even more essential considering that, nowadays, that businesses are gathering and consuming data from a magnitude of sources.
It’s, therefore, necessary to design and build optimal ETL pipelines so that data scientists can do what they’re supposed to do and not focus on building pipelines themselves.
Why Businesses Should Hire Data Engineers?
From the above, it’s clear that data scientists can’t do their jobs effectively without a team of data engineers to design and build the data pipelines. Although there are some overlaps between the work of a data scientist and a data engineer, their jobs focus on different things and require different expertise.
So, when companies hire data scientists only, they end up spending much of the time working on data infrastructure and architecture rather than analyzing data and gaining insights from it. It’s simple, building data pipelines aren’t their main form of expertise, and, as such, they’re far less efficient at it.
As a result, it’s essential that data engineers and data scientists work together, considering the goals of the business, to extract the most value from a business’s data.
Also, because of the sheer magnitude of data that needs to be gathered, transformed, and provided to data scientists in a uniform format, it’s often necessary to hire more than one data engineer for every data scientist.
Here, a common starting point is 2 to 3 data engineers for every data scientist. Likewise, for complex data engineering requirements, this requirement can go up to 4 to 5 data engineers per data scientist.
The Bottom Line
To take full advantage of all the benefits big data offers, businesses must have the right data infrastructure and architecture in place to deal with the increasing amount of data they gather and analyze.
For more information about data engineering or how to hire a data engineer, visit our website or contact us for more details. At Florijn, we focus on leasing out data specialists such as Data Scientists, Data Analysts, Data Architects, Data Engineers, and Machine learning engineers. We only provide top-of-the-class specialists with extensive experience.