What is Data Engineering and Why Is It So Important?

Data engineering is a vital part of our data-driven world, and it would be nearly impossible for organizations to succeed without it. This field makes complex data sets usable, allowing scientists, analysts, and other data consumers to work their magic.
In this helpful guide, you'll learn what data engineering is, what a data engineer does, and what skills they must possess to succeed in their position.
What is data engineering?
Companies large and small have vast amounts of data that need to be sorted to get meaningful answers about their business. This is where data engineering comes into play. Data engineering is the practice of designing and building systems to collect, store, process, and analyze large amounts of raw data to help data scientists and other analysts understand the current state of the business and make data-driven decisions.
Data engineering is a growing field, and the demand for qualified data engineers is growing, which can be attributed to five main reasons:
Data is growing faster than ever before. The volume of data continues to grow exponentially every year. 
Data is becoming increasingly valuable to companies. They are now used in many areas of business — including sales, marketing, and finance — and are used in ways that allow companies to operate more innovatively and efficiently.
The technologies used to handle data are becoming increasingly sophisticated. As data is created in many different systems — such as relational databases, Hadoop, and NoSQL — companies need experts who can work with these more complex technologies.
Businesses are discovering more and more ways to use data to their advantage. Data can help them understand the current state of their business, predict the future, learn more about their customers, reduce risk and create new products. Data engineering is a crucial player in all of these scenarios.
The growth and demand for data engineers continue to grow, creating a snowball effect. As data becomes more complex, this role becomes even more critical. In addition, as the demand for data grows, data engineers will play a more crucial role than ever before.
Why is data engineering necessary?
Companies are producing many new types of data, including more complex data. While companies know that data scientists are needed to make sense of the information, many companies forget that data engineers are required to organize and manage that data.
Problems arise when data scientists are forced to do the job of data analytics engineers. When this happens, they often need help to optimize the data pipelines used for projects. Data engineers are essential because their responsibilities allow data scientists to do their jobs more efficiently.
What does a data engineer do?
The primary responsibility of a data engineer is to receive, store, manage, and transform raw data into an analyzable format so that data scientists and other business analysts can interpret and use it effectively.
As a result, the data engineer will work across multiple departments in the organization because they need to understand what the business hopes to get out of these big data sets. In other words, they must first understand the company or customer's goals for that data. This, in turn, will allow them to build effective processes for discovering and using the data.
In addition to optimizing data discovery, the data engineer also develops dashboards, reports, and other visual assets for the company's stakeholders. In addition, they may be tasked with communicating data trends. In general, however, the role and responsibilities of the data engineer will be somewhat different in large and small organizations. A large company may have several data analysts or scientists to help make sense of the data, while smaller companies may have one data engineer performing all of the duties.

What skills does a data processing engineer need?
There are many unique skills that a data engineer needs to have to fulfill their role. Let's look at each of them below.
General skills
Data processing engineers should have general skills to create software solutions using data. Data engineers may need ten to thirty different tools that vary by industry.
However, some skills are needed regardless of industry. For example, data engineers must always be proficient in SQL. Other necessary skills include:
  • Knowledge of software development
  • Knowledge of distributed systems, such as software engineer and architectural skills
  • Experience with open-source frameworks
  • Mastery of SQL
  • Programming, especially in Python (although other programming languages may be used in different industries)
  • Using Pandas (a Python library used to clean and manipulate data)
  • Use of cloud platforms
  • Skills in statistical analysis
  • Knowledge of data modeling
  • Ability to use visualization and dashboards
While different industries may have different needs, the above skills are a good starting point for data engineers.
Technical Skills
In addition to the skills listed in the previous section, data engineers need several technical skills. Listed below are the different types of technology used by data engineers:
  • Using Oracle, IBM DB2, and Microsoft SQL Server to create relational databases for human resources, financial planning, and CRM.
  • Using Vertica, Teradata, Amazon Redshift, and Sybase IQ to create analytical relational databases for data warehouses and maps.
  • Using MongoDB to create JSON databases for web, social, and mobile networks.
  • Using Redis and Memcached to create key-value storage systems for web, social, and mobile.
  • Using Apache HBase and Apache Cassandra to create columnar databases for IoT and machine data
  • Using Hadoop Distributed File System (HDFS) to create file systems to store data
 How are data engineers different from data scientists?
For some, the roles of data scientist and data engineer may seem confusing, especially since both fields involve gaining valuable insights from data. It's essential to understand the differences between these roles, as data engineer and data scientist differ in many ways.
These differences are evident in their primary focus, responsibilities, languages, tools, software, and education. Let's look at each of these below.
Points of focus
The data engineer's primary focus is to create the infrastructure and architecture that will be used to generate data. On the other hand, the data scientist's primary focus is to perform statistical analysis and use advanced mathematical methods to handle the data being generated.

You see, while the data analyst simply interacts with the data infrastructure and architecture created and maintained by the data engineer, he or she is not responsible for creating and managing it. Instead, the data analyst can be thought of as an internal customer who has to do high-level research to identify trends and relationships between data. They do this using a variety of machines and methods.
Therefore, data engineers can be said to work to support data scientists and data analysts as they provide the infrastructure and tools that are used to solve complex business problems. As a consequence, data scientists depend heavily on data engineers.
We can also look at the critical differences between the responsibilities of data engineers and data analysts. We discussed the responsibilities of data engineers in Chapter 2. In a nutshell, data engineers are responsible for managing, improving, overseeing, and controlling data collection, storage, and delivery across the enterprise. They often have to deal with raw data containing inconsistencies. 
In addition, it is the data analyst's responsibility to access data that the data analysis engineering team has processed. Typically, this data is cleaned and manipulated and then analyzed by data analysts. They do industry research and figure out business questions to determine what needs to be done with that data. To answer these questions, data analysts use large amounts of data from internal and external sources and can further examine the data to identify patterns.

Languages, tools, and software
Data engineers and analysts also vary significantly in the languages, tools, and software they use to perform their duties.
Data scientists primarily work with advanced analytics tools, including R, SPSS, Hadoop, and advanced statistical modeling software, while data engineers focus on products that support these tools.
The main difference here is that the tools and software regularly used by data analysts reflect that role's emphasis on data storytelling and visualization. Whereas for data engineers, the toolkit reflects their responsibilities for designing and building systems and data architecture.
There are also numerous differences between the languages used by engineers and data analysts. Data analysts often use languages such as SPSS, R, Python, SAS, Stata, and Julia in their work. Data engineers, on the other hand, use several different languages, including SQL and Scala.
Although data engineers and data analysts have a general background in computer science, they each study specific areas unique to them.
For example, data analysts often study econometrics, mathematics, statistics, and operations research. This means they have more business knowledge than data engineers, whose background is usually in engineering or computer science.
Data engineers help your business make better data-driven decisions
After reading this guide, you now know what data engineering is and what a data engineer does. As you have learned, there is much more to data engineering than meets the eye. The role of the data engineer, once lumped under the umbrella of data science is now a separate field whose importance grows rapidly every year.
After all, data engineers fulfill the vital role of managing, improving, overseeing, and monitoring data retrieval, storage, and delivery across the enterprise. In doing so, they make vital data more usable by several key stakeholders. Without data engineering, enterprises wouldn't be able to use complex data as they do today.