Enterprise Big Data Engineer
Enterprise Big Data Engineer
Fundamental Approaches for Data Engineering the Creation of Data Pipelines
About the Book
Unlock the full potential of your data with Enterprise Big Data Engineer, the definitive guide to mastering data engineering and building robust data pipelines. This essential resource provides in-depth insights into the core concepts and advanced techniques required to design, develop, and maintain scalable data systems in today’s data-driven enterprises.
Authored by industry experts, this book explores the challenges and solutions in data engineering, from handling the complexities of batch and stream processing to establishing sound storage systems. You'll learn how to create secure and reliable data pipelines that ensure the seamless flow of data across your organization. Additionally, the book delves into crucial topics such as data quality, security, management, and governance, providing you with a comprehensive understanding of the best practices needed to maintain the integrity and reliability of your data assets.
This book is the official guide for the Enterprise Big Data Engineer (EBDE) certification from APMG International, offering all the knowledge you need to succeed in this globally recognized examination. Whether you are preparing for certification or looking to enhance your skills in data engineering, Enterprise Big Data Engineer is your go-to resource for achieving excellence in the field.
About the Author
Table of Contents
- Colophon
- Foreword
- Foreword
- 1. Introduction to Data Engineering
- 1.1 What is Data Engineering?
- 1.2 What does a Data Engineer Do?
- 1.3 The Data Ecosystem of an Organization
- 1.4 Data Engineering vs. Data Analysis and Data Science
- 1.5 Common Challenges in Data Engineering
- 1.6 The Modern Data Engineer
- 2. Structured Data
- 2.1 Introduction to Structured Data and Relational Databases
- 2.2 Fundamentals of Structured Data Management
- 2.3 SQL and Fundamental Querying Techniques
- 2.4 Working with SQL in Python
- 3. Unstructured Data
- 3.1 Introduction to Unstructured Data
- 3.2 Unstructured Data Types and Data Formats
- 3.3 No-SQL Databases
- 4. ETL, Batch and Stream Processing
- 4.1 Introduction to ETL
- 4.2 ETL In Data Engineering
- 4.3 ETL Tools and Technologies
- 4.4 Batch Processing
- 4.5 Stream Processing
- 4.6 ETL, Batch Processing and Stream Processing
- 5. Data Pipelines
- 5.1 Introduction to Data Pipelines
- 5.2 Data Pipeline Architecture
- 5.3 Data Pipeline Patterns
- 5.4 Application Programming Interfaces
- 5.5 Orchestration, Management and Monitoring of Data Pipelines
- 5.6 Building Data Pipelines
- 6. Data Architectures
- 6.1 Introduction to Data Architectures
- 6.2 The Relational Data Warehouse
- 6.3 Data Lake
- 6.4 The Modern Data Warehouse
- 6.5 Data Fabric
- 6.6 Data Lakehouse
- 6.7 Data Mesh
- 7. Machine Learning for Data Engineers
- 7.1 Why Machine Learning for Data Engineers?
- 7.2 Machine Learning Basics: Supervised, Unsupervised and Reinforcement Learning
- 7.3 Fundamental Machine Learning Concepts
- 7.4 Feature Stores and Serving
- 7.5 Deploying Machine Learning Models
- 7.6 Monitoring and Maintenance of ML Models
- 8. Security and Privacy in Data Engineering
- 8.1 What Data Engineers Need to Know About Data Privacy and Data Security
- 8.2 Fundamental Data Privacy Concepts in Data Engineering
- 8.3 Fundamental Security Concepts in Data Engineering
- 8.4 Disaster Recovery and Backup Planning
- 8.5 Culture of Privacy and Security Awareness
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