Data engineering is often referred to as the close cousin of data science. Yet, many people are left wondering: What exactly is data engineering, and what does a data engineer do?
The truth is data engineering is a vital part of our data-driven world, and without it, organizations would find it nearly impossible to succeed. This is because the field makes complex datasets usable, thus allowing data scientists, analysts, and other consumers of data to work their magic.
In this helpful guide, you’ll learn what data engineering is, and what a data engineer does including the skills a data engineer must master to succeed within their position.
Data engineering 101
- 1. What is data engineering?
- 2. Why is data engineering important?
- 3. What does a data engineer do?
- 4. What skills does a data engineer need?
- 5. How do data engineers differ from data scientists?
1. What is data engineering?
Companies both large and small have vast amounts of data to sort through to reveal important answers about their business. This is where data engineering comes into play. Data engineering is the practice of designing and building systems for collecting, storing, processing, and analyzing large amounts of raw data to help data scientists and other analysts understand the current state of their business, and make data-informed decisions.
Data engineering is a growing field, and there is an increasing demand for qualified data engineers which can be attributed to five key reasons:
- Data is growing faster than ever before. The amount of data continues to grow exponentially each year. In fact, the majority (90%) of the data available today was only created in the last two years.
- Data is considered more valuable to companies. It is now being used across many areas of business—including sales, marketing, and finance—and in ways that allow businesses to operate more innovatively and effectively.
- The technologies being used for data are becoming more complex. Because data is being created across many different systems—such as relational databases, Hadoop, and NoSQL—companies require specialists to be able to work across these more complicated technologies.
- Businesses are discovering more ways to use data to their advantage. Data can help them to understand the current state of their business, predict the future, learn more about their customers, reduce risks, and create new products. Data engineering is the key player in all of these scenarios.
- Growth and demand continue to rise for data engineers, thus creating a snowball effect. As data becomes more complex, this role becomes even more vital. Additionally, as demands for data increase, data engineers will be more critical than ever before.
2. Why is data engineering important?
Companies have produced massive volumes of new types of data, including more complicated data. Although companies know that data scientists are needed to make sense of the information, many companies forget that data engineers are necessary to organize and manage this data.
Problems occur when data scientists are forced to do the job of data engineers. When this happens, they often fail to optimize the data pipelines used for projects. Data engineers are important because their duties allow data scientists to do their work more effectively.
3. What does a data engineer do?
A data engineer’s main responsibility is to retrieve, store, manage and transform raw data into an easy-to-analyze format for data scientists and other business analysts to interpret, and effectively use.
As a result, a data engineer will work across multiple departments within an organization, as they need to understand what the business is hoping to gain from these large datasets. In other words, they must first understand a company’s or client’s goals in relation to that data. This, in turn, allows them to build effective processes for uncovering and using the data.
Along with optimizing data retrieval, a data engineer also develops dashboards, reports, and other visual assets for a company’s stakeholders. Additionally, they may be tasked with communicating data trends. All in all, however, a data engineer’s role and responsibilities will differ slightly across large or small organizations. A big company may have several data analysts or scientists onboard to assist with understanding data, while smaller companies might rely on a single data engineer to fulfill all responsibilities.
To fulfill these responsibilities, a data engineer must undertake a number of tasks. These include:
- Acquisition: Sourcing the data from various systems
- Cleansing: Identifying and fixing errors
- Conversion: Changing data from one format to another
- Disambiguation: Interpreting data that has various meanings
- De-duplication: Eliminating duplicate copies of data
Find a data engineer for hire
4. What skills does a data engineer need?
There are many specialized skills that a data engineer must possess to fulfill their role. Let’s take a look at each of these below.
Data engineers need to have general skills that allow them to create software solutions that involve data. There are between ten and thirty different tools data engineers may need to use, which vary between industries.
However, some skills are necessary regardless of the industry. For example, data engineers must always be proficient in SQL. Other necessary skills include:
- Background with software engineering
- Knowledge of distributed systems like engineer and software architect skills
- Experience with open frameworks
- Proficiency using SQL
- Programming, especially with Python (although other programming languages may be used by different industries)
- Use of Pandas (a Python library used to clean and manipulate data)
- Use of cloud platforms
- Statistical analysis skills
- Knowledge of data modeling
- Ability to use visualization and dashboards
While there may be different needs between industries, the above skills are a good starting point for data engineers.
In addition to the skills listed in the previous section, data engineers need a number of technical skills. The following are different types of technologies used by data engineers:
- Use of Oracle, IBM DB2, and Microsoft SQL Server to create relational databases for HR, financial planning, and CRM
- Use of Vertica, Teradata, Amazon Redshift, and Sybase IQ to create analytical relational databases for data warehouses and data marts
- Use of MongoDB to create JSON databases for web, social, and mobile
- Use of Redis and Memcached to create key-value systems for web, social, and mobile
- Use of Apache HBase and Apache Cassandra to create columnar databases for IoT and machine data
- Use of Hadoop Distributed File System (HDFS) to create file systems for data storage
- Use of Azure Blob Store and Amazon S3 to create object stores for data storage
- Use of Microsoft Excel to create spreadsheets for desktop data analysis
The exact applications used will vary between industries and companies, however.
5. How do data engineers differ 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 important to understand the distinct differences between these roles, as a data engineer and data scientist differ in many ways.
These differences are highlighted in their points of focus, responsibilities, languages, tools, software, and educational background. Let’s examine each below.
Points of focus
A data engineer’s main focus is building infrastructure and architecture to be used for generating data. On the other hand, a data scientist’s primary focus is performing statistical analysis and using advanced mathematics on the data that is generated.
You see, although a data scientist simply interacts with the data infrastructure and architecture built and maintained by a data engineer; they are not in charge of actually creating and managing it. Instead, a data scientist can be viewed as an internal client who must perform high-level research to identify trends and relations among data. They do this using a variety of machines and methods.
Therefore, we can say that data engineers work to support data scientists and data analysts, as they provide the infrastructure and tools that are used to deliver solutions to complex business problems. As a result, data scientists heavily depend on data engineers.
We can also look at the key differences between the responsibilities of data engineering and data science. We discussed the responsibilities of data engineers in Chapter 2. In a nutshell, data engineers are responsible for managing, enhancing, overseeing, and monitoring the retrieval, storage, and delivery of data throughout the business. Often, they will be dealing with raw data that contains inconsistencies. It’s therefore also part of their job to recommend and implement ways to improve the reliability, efficiency, and quality of this data so that it is “usable” to data scientists.
Alternatively, a data scientist’s responsibilities include gaining access to the data that has been processed by the data engineering team. Usually, this data has undergone a round of cleaning and manipulation, and it is then further analyzed by data scientists. They will perform industry research and establish a business’s questions to determine what they need to achieve with this data. Data scientists will also leverage large amounts of data from internal and external sources to answer these questions and may even further examine this data to uncover patterns.
When we examine the key differences across the responsibilities of a data engineer and a data scientist, it boils down to this: A data scientist needs to know about statistics, mathematics, and machine learning to build predictive models, whereas a data engineer will work with database systems, data API and tools for ETL purposes. Additionally, they will be involved in setting up data warehouse solutions and data modeling.
Languages, tools, and software
Data engineers and data scientists also differ greatly in the languages, tools, and software they use to perform their responsibilities.
Data scientists mostly work with advanced analysis tools including R, SPSS, Hadoop, and advanced statistical modeling software, whereas data engineers are focused on the products which support those tools. Therefore, a data engineer’s armory includes Spark, Hadoop, MongoDB, and other data organization services, as covered in Chapter 4.
The main difference here is that the tools and software regularly used by data scientists reflect the role’s emphasis on storytelling and data visualization. For data engineers, however, their toolkit is reflective of their responsibility to design and build data systems and architecture.
There are also multiple differences between the languages used by data engineers and data scientists. Data scientists frequently use languages such as SPSS, R, Python, SAS, Stata, and Julia to perform their work. Data engineers, on the other hand, utilize some different languages, including SQL and Scala.
While data engineers and data scientists both have educational backgrounds in computer sciences, each has specific fields of study that are unique to them.
Data scientists have often studied econometrics, mathematics, statistics, and operations research, for example. This means they have more business expertise than data engineers, whose background often lies in engineering or computer engineering.
Data engineers help your business make better decisions with data
After reading this guide, you now know exactly what is data engineering, along with what a data engineer does. As you’ve learned, there’s a lot more to data engineering than meets the eye. Once lumped under the umbrella of data science, the role of a data engineer is now a distinct field that is rapidly growing in importance each year.
As the volume of data continues to significantly increase, it comes as no surprise that data engineering is only predicted to rise in significance for businesses small and large. After all, data engineers have the vital role of managing, enhancing, overseeing, and monitoring the retrieval, storage, and delivery of data throughout the business. In doing so, they make vital data more usable for a number of key stakeholders. Without data engineering, businesses simply wouldn’t be able to utilize complex data in the ways they do today.