Cracking the Data Engineering Interview : Land Your Dream Job with the Help of Resume-Building Tips, over 100 Mock Questions, and a Unique Portfolio

Cracking the Data Engineering Interview Land Your Dream Job with the Help of Resume-Building Tips, over 100 Mock Questions, and a Unique Portfolio

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...

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Detalles Bibliográficos
Otros Autores: Bryan, Kedeisha, author (author), Ransome, Taamir, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing [2023]
Edición:First edition
Materias:
Database management > Problems, exercises, etc.
Big data > Problems, exercises, etc.
Employment interviewing > Problems, exercises, etc.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009781237406719
Tabla de Contenidos:
  • 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.

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