In the past two decades, the digital world has been flooded with data. Most companies have completed digital transformations, and more and more users leave their digital footprint behind every day. All of this information can be turned into useful knowledge that allows companies to make data-driven decisions. This is where the process of data engineering takes place.
Data engineering is a branch of data science. Data science is a broad term that includes all of the tasks that make data valuable - from gathering the data to storing, analyzing, and visualizing it. It’s important to remember that data in its raw form is not really valuable for making business decisions. Only once it’s been cleaned, verified, transformed, organized, managed, and analyzed, it’s turned into knowledge that makes a company data-driven.
Data engineering is one of the first steps of the data science process. It is the task of making data usable for data analysts and data scientists.
Do you need data engineering services?
Fig. 1 The data science hierarchy of needs.
This pyramid represents the hierarchy of needs for a data-driven organization. Data engineering consists of the “collect”, “move/store”, and “explore/transform” parts. It makes perfect sense taking into consideration that engineers, in general, build things - and data engineers build pipelines and solutions that allow for collecting, moving, storing, and transforming data.
The need for data engineering and data science has been increasing since the early 2010s. With the rise of social media and user-generated content, companies realized how much valuable information analyzing this data could bring.
Most importantly, data engineering provides the tools that allow data scientists and analysts to understand the user - and once you understand them, you know their needs and provide the products they actually want to use. Data engineering makes sure you make decisions based on facts.
Data science also helps to predict market trends and customer behavior and analyze your competition. But let’s note that we’re talking about enormous amounts of data - server logs from hundreds of thousands of users, records from IoT devices, financial reports, photos, and videos published online. All of this data comes from different sources and in various formats. Finding a way of storing, categorizing, and unifying them is exactly what a data engineer does.
Learn more about what is big data from our previous article.
Data engineering is what allows companies to become data-driven. It’s the only way of gathering and transforming factual, accurate data. Without reliable dataflows, organized data storage systems, and adequately prepared data, organizations are not able to use it for making decisions based on evidence.
To put it simply, data engineers transform raw data and make it usable for other users, for example, data scientists, data analysts, or machine learning specialists.
Fig. 2 The process of data engineering
The data engineering process consists of many steps. To understand them, let’s take a look at some of the tasks of data engineers' work:
- Managing data storage
Data engineers need to find ways of storing structured and unstructured data coming from various sources. So they need to manage a data warehouse or data lake and categorize files in a way that makes them easily accessible to other users.
- Moving data
Once data is gathered, it needs to be moved from different files to databases, cloud storage systems, or software for data analytics. This is when data engineers build data pipelines and design data flow. A big challenge for data engineers is building data pipelines that work with different data types.
- Transforming data
Because of data variety, data engineers need to transform data into formats that are more efficient for storing, using and analyzing. That might mean turning unstructured data into structured data via machine learning algorithms or cleansing data of any unreliable records.
- Data modeling
Modeling data is usually the data scientists or analysts' duty, but data engineers need some knowledge in this field as well. When they understand data modeling requirements, they can design databases, architecture, and relations between bits of data that allow for analytics according to business needs.
Including a data engineering process in an organization results in more efficient data use. The data becomes better organized and accessible to business intelligence, machine learning, and data analytics specialists.
Data engineers differ from data scientists in many ways. While there was a time when these terms were used interchangeably, nowadays, there is a significant distinction between their skills.
Data engineering skills include programming, mostly with Python and SQL, and software engineering skills, such as distributed systems and service-oriented architecture. Some of the tools they use include cloud platforms, data warehouses, and many open frameworks. You can find out specific examples of tools in our article about top data engineering tools.
Data scientists work with data after it's been processed by data engineering teams. They also have a slightly different approach. A data scientist is more focused on mathematics, statistics and analytics.
While data engineers are also knowledgeable in these fields, their primary focus is designing and building systems for storing and transforming data.
The data science team structure differs depending on the organization’s business needs. To conduct complex data processing, both of these professions are needed. There are many other roles that should be included in a data science team, for example, data architects, data analysts, database administrators, business intelligence engineers, data modelers, and data quality engineers.
Data engineering is a process that has to happen in any company that wants to stay competitive. With innovative solutions and adequate data infrastructure, an organization is able to understand its environment and its customers. Data engineering is a tool for making better decisions. It will provide material for business intelligence, data modeling, and analytics. Data engineering is a step that no present-day company can skip.