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Books - the good old way of learning. Some people may prefer to do courses, whereas some people just want to bury their heads in a book. In this article, I will be going through the roadmap of books you need in order to become a Data Engineer. 

Before I start that, let’s quickly overview what a Data Engineer is

What is a Data Engineer?

Data Engineering is a part of Data Science. Data Scientists are responsible for exploring the data and then building machine learning algorithms to solve the task or problem. Whilst Data Engineers' focus is more concerned about making these algorithms work effectively and the creation of data pipelines. 

Data Engineers are responsible for setting up and maintaining the organization's data infrastructure. Other responsibilities include:

• Data acquisition
• Developing, building, testing, and maintaining architectures that are in line with the business requirements
• Improving the data accuracy, efficiency, and quality
• Perform predictive and prescriptive modeling
• Implementing analytical tools, machine learning, and statistical methods
• Communicate findings with stakeholders

The Books You Need to Get There

The Fundamentals

So let’s start off with the fundamentals of Data Engineering. 

• Fundamentals of Data Engineering: Plan and Build Robust Data Systems
• 97 Things Every Data Engineer Should Know: Collective Wisdom from the Experts
• The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
• Data Pipelines Pocket Reference: Moving and Processing Data for Analytics
• Data Engineering With Python

More Specialised

Once you understand the fundamentals well, your next aim would be to become more specialised in your area of interest. 

• Spark: The Definitive Guide: Big Data Processing Made Simple
• Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
• Kafka - The Definitive Guide: Real-Time Data and Stream Processing at Scale
• Cassandra - The Definitive Guide, 3e: Distributed Data at Web Scale
• Big Data: Principles and best practices of scalable real-time data systems

Data Architecture and Management

• Data Mesh: Delivering Data-Driven Value at Scale
• Foundations for Architecting Data Solutions: Managing Successful Data Projects
• Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale
• Data Management at Scale: Best Practices for Enterprise Architecture

Go Above and Beyond

Depending on what position you want to be at as a Data Engineer, there is always more that you can learn. If it’s to land yourself a specific job or just have the knowledge, below are some books that can help you achieve this

• Think Like An Engineer: Inside the Minds that are Changing our Lives
• Data Governance: The Definitive Guide: People, Processes, and Tools to Operationalize Data Trustworthiness
• Team Topologies: Organizing Business and Technology Teams for Fast Flow

Wrapping It Up

When it comes to the fundamentals, you want to be able to cover all those aspects and topics. They will provide you with the foundation of your Data Engineering career and from there onwards, you can choose what you are more interested in and where you see yourself in the next 5-10 years. 

If you are interested in taking some courses whilst you’re learning to test your knowledge, have a read of this article: Free Data Engineering Courses

Good luck!

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.