Preface

Within the domain of data, a distinct group of experts known as data engineers are devoted to ensuring that data is not merely accumulated, but rather refined, dependable, and prepared for analysis. Due to the emergence of big data technologies and the development of data-driven decision-making, the significance of this position has increased substantially, rendering data engineering one of the most desirable careers in the technology sector. However, the trajectory toward becoming a prosperous data engineer remains obscure for many.

Cracking the Data Engineering Interview serves as a printed mentor. Providing ambitious data engineers with the necessary information, tactics, and self-assurance to enter this ever-changing industry. The organization of this book facilitates your progression in comprehending the domain of data engineering, attaining proficiency in its fundamental principles, and equipping yourself to confront the intricacies of its interviews.

Part 1 of this book delves into the functions and obligations of a data engineer and offers advice on establishing a favorable impression before the interview. This includes strategies, such as presenting portfolio projects and enhancing one’s LinkedIn profile. Parts 2 and 3 are devoted to the technical fundamentals, guaranteeing that you will possess a comprehensive understanding of the essential competencies and domains of knowledge, ranging from the intricacies of data warehouses and data lakes to Python programming. In Part 4, an examination is conducted of the essential tools and methodologies that are critical in the contemporary data engineering domain. Additionally, a curated compilation of interview inquiries is provided for review.

Who this book is for

If you are an aspiring Data Engineer looking for a guide on how to land, prepare, and excel in data engineering interviews, then this book is for you.

You should already understand and should have been exposed to fundamentals of Data Engineering such as data modeling, cloud warehouses, programming (python & SQL), building data pipelines, scheduling your workflows (Airflow), and APIs.

What this book covers

Chapter 1, The Roles and Responsibilities of a Data Engineer, explores the complex array of responsibilities that comprise the core of a data engineer’s role. This chapter unifies the daily responsibilities, long-term projects, and collaborative obligations associated with the title, thereby offering a comprehensive perspective of the profession.

Chapter 2, Must-Have Data Engineering Portfolio Projects, this chapter helps you dive deep into a selection of key projects that can showcase your prowess in data engineering, offering potential employers tangible proof of your capabilities.

Chapter 3, Building Your Data Engineering Brand on LinkedIn, this chapter shows you how to make the most of LinkedIn to show off your accomplishments, skills, and goals in the field of data engineering.

Chapter 4, Preparing for Behavioral Interviews, Along with technical skills, the most important thing is that you can fit in with your team and the company’s culture. There are tips in this chapter on how to do well in behavioral interviews so that you can talk about your strengths and values clearly.

Chapter 5, Essential Python for Data Engineers, Python is still an important tool for data engineers. This chapter will help you learn about the Python ideas, libraries, and patterns that every data engineer needs to know.

Chapter 6, Unit Testing, In data engineering, quality assurance is a must. This chapter will teach you the basics of unit testing to make sure that your data processing scripts and pipelines are reliable and strong.

Chapter 7, Database Fundamentals, At the heart of data engineering lies the database. In this chapter you will acquaint yourself with the foundational concepts, types, and operations of databases, establishing a solid base for advanced topics.

Chapter 8, Essential SQL for Data Engineers, SQL is the standard language for working with data. This chapter will help you learn the ins and outs of SQL queries, optimizations, and best practices so that getting and changing data is easy.

Chapter 9, Database Design and Optimization, It’s both an art and a science to make databases work well. This chapter will teach you about advanced design principles and optimization methods to make sure your databases are quick, scalable, and reliable.

Chapter 10, Data Processing and ETL, Turn raw data into insights that can be used. In this chapter we will learn about the tools, techniques, and best practices of data processing in this chapter, which is about the Extract, Transform, Load (ETL) process.

Chapter 11, Data Pipeline Design for Data Engineers, A data-driven organization needs to be able to easily move data from one place to another. In this chapter you will learn about the architecture, design, and upkeep of data pipelines to make sure that data moves quickly and reliably.

Chapter 12, Data Warehouses and Data Lakes, Explore the huge world of ways to store data. This chapter teaches you the differences between data warehouses and data lakes, as well as their uses and architectures, to be ready for the challenges of modern data.

Chapter 13, Essential Tools You Should Know About, It’s important to have the right tool. In this chapter you will learn how to use the most important tools in the data engineering ecosystem, from importing data to managing it and keeping an eye on it.

Chapter 14, Continuous Integration/Continuous Development for Data Engineers, Being flexible is important in a world where data is always changing. In data engineering and in this chapter, you will learn how to use CI/CD to make sure that data pipelines and processes are always up-to-date and running at their best.

Chapter 15, Data Security and Privacy, It’s important to be responsible when you have a lot of data. This chapter will teach you about the important issues of data security and privacy, and get to know the best ways to protect your data assets and the tools you can use to do so.

Chapter 16, Additional Interview Questions, Getting ready is half the battle won. This chapter comprises of carefully chosen set of interview questions that cover a wide range of topics, from technical to situational. This way, you’ll be ready for any surprise that comes your way.

To get the most out of this book

You will need to have a basic understanding of Microsoft Azure.

Software/hardware covered in the book	Operating system requirements
Microsoft Azure	Windows, macOS, or Linux
Amazon Web Services	Windows, macOS, or Linux
Python	Windows, macOS, or Linux

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Cracking-Data-Engineering-Interview-Guide. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

from scrape import *import pandas as pd
from sqlalchemy import create_engine
import psycopg2

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “You can get your connection string from your Connect tab and fix it into the format shown previously.”

Tips or important notes

Appear like this.

Get in touch

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