Cracking the Data Engineering Interview : Land Your Dream Job with the Help of Resume-Building Tips, over 100 Mock Questions, and a Unique Portfolio / Kedeisha Bryan and Taamir Ransome.
Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employers Key Features Develop your own brand, projects, and portfolio with expert help to stand out in the interview round Get a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modeling Practice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical rounds Purchase of the print or Kindle book includes a free PDF eBook Book Description Preparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey. The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you'll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you'll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions. By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role. What you will learn Create maintainable and scalable code for unit testing Understand the fundamental concepts of core data engineering tasks Prepare with over 100 behavioral and technical interview questions Discover data engineer archetypes and how they can help you prepare for the interview Apply the essential concepts of Python and SQL in data engineering Build your personal brand to noticeably stand out as a candidate Who this book is for If you're an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite.
| Person(en) | , |
|---|---|
| Ausgabe | First edition. |
| Ort, Verlag, Jahr |
Birmingham, England
: Packt Publishing
, 2023
|
| Umfang | 1 online resource (0 pages) |
| ISBN | 9781837631070 1837631077 |
| Sprache | Englisch |
| Zusatzinfo | Includes index. |
| Zusatzinfo | Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Landing Your First Data Engineering Job -- 1 -- Chapter 1: The Roles and Responsibilities of a Data Engineer -- Roles and responsibilities of a data engineer -- Responsibilities -- An overview of the data engineering tech stack -- Summary -- 2 -- Chapter 2: Must-Have Data Engineering Portfolio Projects -- Technical requirements -- Must-have skillsets to showcase in your portfolio -- Ability to ingest various data sources -- Data storage -- Data processing -- Cloud technology -- Portfolio data engineering project -- Scenario -- Summary -- 3 -- Chapter 3: Building Your Data Engineering Brand on LinkedIn -- Optimizing your LinkedIn profile -- Your profile picture -- Your banner -- Header -- Crafting your About Me section -- Initial writing exercise -- Developing your brand -- Posting content -- Building your network -- Sending cold messages -- Summary -- 4 -- Chapter 4: Preparing for Behavioral Interviews -- Identifying six main types of behavioral questions to expect -- Assessing cultural fit during an interview -- Utilizing the STARR method when answering questions -- Example interview question #1 -- Example interview question #2 -- Example interview question #3 -- Example interview question #4 -- Example interview question #5 -- Reviewing the most asked interview questions -- Summary -- Part 2: Essentials for Data Engineers Part I -- 5 -- Chapter 5: Essential Python for Data Engineers -- Must-know foundational Python skills -- SKILL 1 - understand Python's basic syntax and data structures -- SKILL 2 - understand how to use conditional statements, loops, and functions -- SKILL 3 - be familiar with standard built-in functions and modules in Python -- SKILL 4 - understand how to work with file I/O in Python. SKILL 5 - functional programming -- Must-know advanced Python skills -- SKILL 1 - understand the concepts of OOP and how to apply them in Python -- SKILL 2 - know how to work with advanced data structures in Python, such as dictionaries and sets -- SKILL 3 - be familiar with Python's built-in data manipulation and analysis libraries, such as NumPy and pandas -- SKILL 4 - understand how to work with regular expressions in Python -- SKILL 5 - recursion -- Technical interview questions -- Python interview questions -- Data engineering interview questions -- General technical concept questions -- Summary -- Chapter 6: Unit Testing -- Fundamentals of unit testing -- Importance of unit testing -- Unit testing frameworks in Python -- Process of unit testing -- Must-know intermediate unit testing skills -- Parameterized tests -- Performance and stress testing -- Various scenario testing techniques -- Unit testing interview questions -- Summary -- Chapter 7: Database Fundamentals -- Must-know foundational database concepts -- Relational databases -- NoSQL databases -- OLTP versus OLAP databases -- Normalization -- Must-know advanced database concepts -- Constraints -- ACID properties -- CAP theorem -- Triggers -- Technical interview questions -- Summary -- Chapter 8: Essential SQL for Data Engineers -- Must-know foundational SQL concepts -- Must-know advanced SQL concepts -- Technical interview questions -- Summary -- Part 3: Essentials for Data Engineers Part II -- Chapter 9: Database Design and Optimization -- Understanding database design essentials -- Indexing -- Data partitioning -- Performance metrics -- Designing for scalability -- Mastering data modeling concepts -- Technical interview questions -- Summary -- Chapter 10: Data Processing and ETL -- Fundamental concepts -- The life cycle of an ETL job. Practical application of data processing and ETL -- Designing an ETL pipeline -- Implementing an ETL pipeline -- Optimizing an ETL pipeline -- Preparing for technical interviews -- Summary -- Chapter 11: Data Pipeline Design for Data Engineers -- Data pipeline foundations -- Types of data pipelines -- Key components of a data pipeline -- Steps to design your data pipeline -- Technical interview questions -- Summary -- Chapter 12: Data Warehouses and Data Lakes -- Exploring data warehouse essentials for data engineers -- Architecture -- Schemas -- Examining data lake essentials for data engineers -- Data lake architecture -- Data governance and security -- Data security -- Technical interview questions -- Summary -- Part 4: Essentials for Data Engineers Part III -- Chapter 13: Essential Tools You Should Know -- Understanding cloud technologies -- Major cloud providers -- Core cloud services for data engineering -- Identifying ingestion, processing, and storage tools -- Data storage tools -- Mastering scheduling tools -- Importance of workflow orchestration -- Apache Airflow -- Summary -- Chapter 14: Continuous Integration/Continuous Development (CI/CD) for Data Engineers -- Understanding essential automation concepts -- Test automation -- Deployment automation -- Monitoring -- Mastering Git and version control -- Git architecture and workflow -- Branching and merging -- Collaboration and code reviews -- Understanding data quality monitoring -- Data quality metrics -- Setting up alerts and notifications -- Pipeline catch-up and recovery -- Implementing CD -- Deployment pipelines -- Infrastructure as code -- Technical interview questions -- Summary -- Chapter 15: Data Security and Privacy -- Understanding data access control -- Access levels and permissions -- Authentication versus authorization -- RBAC -- Implementing ACLs. Mastering anonymization -- Masking personal identifiers -- Applying encryption methods -- Encryption basics -- SSL and TLS -- Foundations of maintenance and system updates -- Regular updates and version control -- Summary -- Chapter 16: Additional Interview Questions -- Index -- Other Books You May Enjoy. |
| Online-Zugang | ProQuest Ebook Central PDA (EPDA) |
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