What is Data Engineering? Skills, Tools, and Certifications
Data engineering is the process of designing and implementing solutions to collect, store, and analyze large amounts of data. This process is generally called “Extract, Transfer, Load” or ETL.
The data then gets prepared in formats to be used by people such as business analysts, data analysts, and data scientists. The format of the data will be different depending on the intended audience. Part of the Data Engineer’s role is to figure out how to best present huge amounts of different data sets in a way that an analyst, scientist, or product manager can analyze.
What does a data engineer do?
A data engineer is an engineer who creates solutions from raw data. A data engineer develops, constructs, tests, and maintains data architectures.
Let’s review some of the big picture concepts as well finer details about being a data engineer.
What does a data engineer do – the big picture
Data engineers will often be dealing with raw data. Many of them are already familiar with SQL or have experience working with databases, whether they’re relational or non-relational. They need to understand common data formats and interfaces, and the pros and cons of different storage options.
Data engineers are responsible for transforming data into an easily accessible format, identifying trends in data sets, and creating algorithms to make the raw data more useful for business units.
Data engineers have the ability to convert raw data into useful insights. Data scientists are very grateful for the work done by data engineers to prepare data so that they can turn it into insights.
What does a data engineer do – details
The architecture that a data engineer will be working on can include many components. The architecture can include relational or non-relational data sources, as well as proprietary systems and processing tools. The data engineer will often add services and tools to the architecture in order to make sure that data scientists have access to it at all times.
Earlier we mentioned ETL or extract, transform, load. Data engineers use the data architecture they create to load, extract and transform raw data. Raw data can often contain errors and anomalies such as duplicates, incompatibilities, and mismatches. Data engineers will review the data and suggest ways to improve its quality and reliability.
How data engineers use tools – a basic example
An import tool that can handle data could be used to ignore rows not meeting certain criteria and only import those rows. Data could be a string, a number, or a particular length.
You could use a Python script to convert or replace specific characters within those fields. Creative data engineers will be able to identify problems in data quickly and will be able to find the best solutions.
How to become a data engineer
Here’s a 6-step process to become a data engineer:
- Understand data fundamentals
- Get a basic understanding of SQL
- Have knowledge of regular expressions (RegEx)
- Have experience with the JSON format
- Understand the theory and practice of machine learning (ML)
- Have experience with programming languages
1. Understand data fundamentals
Understanding how data is stored and structured by machines is a foundation. For example, it’s good to be familiar with the different data types in the field, including:
- variables
- varchar
- int char
- prime numbers
- int numbers
Also, named pairs and their storage in SQL structures are important concepts. These fundamentals will give you a solid foundation in data and datasets.
2. Get a basic understanding of SQL
A second requirement is to have a basic understanding of SQL. Knowing SQL means you are familiar with the different relational databases available, their functions, and the syntax they use.
3. Have knowledge of regular expressions (RegEx)
It is essential to be able to use regular expressions to manipulate data. Regular expressions can be used in all data formats and platforms.
4. Have experience with the JSON format
It’s good to have a working knowledge of JSON. For example, you can learn about how JSONs are integral to non-relational databases – especially data schemas, and how to write queries using JSON.
5. Understand the theory and practice of machine learning (ML)
A good understanding of the theory and practice of machine learning will be helpful as you architect solutions for data scientists. This is important even if working with ML models may not be part of your daily routine.
6. Have experience with programming languages
Having programming knowledge is more of an option than a necessity but it’s definitely a huge plus. Some good options are Python (because of its flexibility and being able to handle many data types), as well as Java, Scala, and Go.
Soft skills for data engineering
Problem solving using data-driven methods
It’s key to have a data-driven approach to problem-solving. Rely on the real information to guide you.
Ability to communicate complex concepts and visualize them
Data engineers will need to collaborate with customers, integration partners, and internal technology teams. Sharing your insights with people of various backgrounds and understanding what they are trying to convey is always helpful.
Strong sense of ownership
Take initiative to solve complex problems, because that’s what this job is about. You will be given a framework and a job goal – it’s up to you to figure out the rest.
Tools and resources for data engineering
The following are tools that are important in data engineering, along with courses that explain how to use them and where they fit in the job role.
Databases, relational and non-relational
It’s good to understand database architectures. Some basic real-world examples are:
- Relational, SQL database: e.g. Microsoft SQL Server
- Document-oriented database: MongoDB (classified as NoSQL)
The Basics of Data Management, Data Manipulation and Data Modeling
This learning path focuses on common data formats and interfaces. The path will help you understand common data formats you might encounter as a data engineer, starting with SQL.
MongoDB Configuration and Setup
Watch an example of deploying MongoDB to understand its benefits as a database system.
Apache Kafka
Amazon MSK and Kafka Under the Hood
Apache Kafka is an open-source streaming platform. Learn about the AWS-managed Kafka offering in this course to see how it can be more quickly deployed.
Apache Spark
In this lecture, you’ll learn about Spark – an open-source analytics engine for data processing. You learn how to set up a cluster of machines, allowing you to create a distributed computing engine that can process large amounts of data.
Apache Hadoop
Introduction to Google Cloud Dataproc
Hadoop allows for distributed processing of large datasets. In this course, get the real-world context of Hadoop as a managed service as part of Google Cloud Dataproc, used for big data processing and machine learning.
Python
Introduction to Python for Programmers
Python is a powerful and flexible scripting language that can handle many data types. This course is a quick summary of the theory and practice of Python for users who already have a programming background.
Java
Java is a robust, complicated, but proven language that forms the base of much data engineering work. This learning path covers the basics of Java, including syntax, functions, and modules. These courses teach you how to write Java applications and functions using object-oriented principles.
Data Engineering Certifications
There’s probably no better way to both educate yourself in data engineering and prove to employers what you know than through certifications from the big cloud providers.
The following certification learning paths provide updated, proven, detailed methods to learn everything you need about data engineering.
AWS Data Engineering
AWS Certified Data Analytics Specialty (DAS-01) Certification Preparation
This learning path covers the five domains of the exam. This includes understanding the AWS data analysis services and how they interact with one another. It also explains how AWS data services fit into the data lifecycle of storage, processing, visualization, and storage.
Azure Data Engineering
Foundational Certification
DP-900 Exam Preparation: Microsoft Azure Data Fundamentals
This certification path is for technical as well as non-technical individuals who wish to show their knowledge about core data concepts and how these are implemented using Azure data services.
You’ll learn about the basics of data concepts, relational and non-relational Azure data, and how to describe an Azure analytics workload.
Associate Certifications
DP-203 Exam Preparation: Data Engineering on Microsoft Azure
This certification learning path will teach you how to manage and deploy a range of Azure data solutions. This exam will test your knowledge in four areas: designing and building data storage; designing, developing and managing data processing; designing and monitoring security; and optimizing data storage.
Google Cloud Data Engineering
Google Data Engineer Exam – Professional Certification Preparation
This certification learning path helps you understand and work with BigQuery, Google’s managed cloud data warehouse. You’ll learn how to load, query, and process your data. You’ll learn how to use machine learning for analysis, build data pipelines, and use BigTable for big data applications.
What is Big Data Engineering?
You can call it a buzzword, but big data engineering is the umbrella term for everything in the data engineering world. Typically in big data engineering, you have to interface with huge data processing systems and databases in large-scale computing environments. These environments are often cloud-based to take advantage of the distributed, scalable nature of cloud solutions, as well as turnkey set up in order to speed up development and deployment.
What’s the difference between a data engineer and a data scientist?
These roles can be combined, but they work well together. Data scientists and data engineers are two roles that require different skills and have distinct tasks.
Data engineers design, test and maintain data. Data scientists organize and manipulate data in order to gain insight. Data engineers are responsible for creating data that scientists can use.
Although things aren’t always perfectly separated in the real world, think of the data engineer as the controller of the data and its infrastructure, and the data scientist as the specialist who gathers insights from the curated data.
Both roles are important and need cooperation and respect to work well together and achieve a successful outcome.
How much do data engineers make?
As of early 2022, some of the top salary sites online show the following numbers for an average base salary for a data engineering role in the United States:
- Glassdoor: $112,000
- Payscale: $93,000
- Indeed: $116,000
FAQ
Is data engineering easy?
It’s not easy, and it’s not the easiest role to get into, but it’s definitely interesting and rewarding. Some industry experts complain that there is a huge gap between self-educated and actual-world data engineers. This is due to a lack of relevant college or university programs that prepare you for data engineering.
Do you need math for data engineering?
In general, data engineering is not math-heavy. It would be helpful to be familiar with statistics and probability to get a sense of what data scientists in your team will do. A good understanding of problem solving from a software engineering and cloud architect point of view will help for daily issues.
Are data engineers in demand?
Yes, data engineers are in demand, especially as companies realize that the hype of data science is built on the foundation of work from data engineers. The most marketable data engineers have multi-cloud experience to help them make an impact in any environment.
Do data engineers code?
Yes, data engineers can expect to do a lot of data pipeline coding so they should be comfortable with programming languages and debugging issues. It’s helpful to be fluent in SQL, Python, and R.
