Modern Data Engineering
Build Reliable Pipelines, Warehouses, and Analytics Systems for Real-World Data WorkflowsBy Owen Fletcher
Length7h 33m
About this audiobook
**Master the foundations of modern data engineering and learn how to build reliable, scalable data systems from the ground up.**
Data is one of the most valuable assets in today's organizations—but raw data alone has little value. It must be collected, organized, transformed, tested, and delivered before it can power analytics, business intelligence, dashboards, and artificial intelligence. That is where modern data engineering comes in.
**Modern Data Engineering** provides a practical, beginner-friendly guide to the principles, architectures, and workflows used to build dependable data platforms. Rather than focusing on a single vendor or tool, this book teaches the core concepts that remain valuable across today's rapidly evolving data ecosystem.
Inside, you'll learn how to:
* Understand the role of a modern data engineer
* Design reliable data pipelines from source to analytics
* Work with databases, data warehouses, data lakes, and lakehouses
* Build efficient ETL and ELT workflows
* Ingest data from APIs, databases, files, and SaaS platforms
* Store and organize data using modern file formats and partitioning strategies
* Transform raw data into analytics-ready datasets using SQL
* Design fact tables, dimension tables, and star schemas
* Orchestrate automated workflows and pipeline scheduling
* Understand batch and real-time data processing
* Improve data quality through testing and validation
* Implement governance, metadata management, security, and access control
* Build scalable cloud-based data engineering solutions
* Complete an end-to-end data engineering project suitable for your professional portfolio
Whether you're an aspiring data engineer, software developer, data analyst, cloud professional, or IT student looking to expand your technical skills, this book offers a structured learning path that combines essential theory with practical design principles.
By the end of the book, you'll understand how modern data platforms are built, how data flows through an organization, and how to design scalable, trustworthy data systems that support informed decision-making and future-ready applications.
If you're ready to build the skills behind today's data-driven organizations, **Modern Data Engineering** is your practical guide to getting started.
Audiobook details
GenreTechnology
Length7 hrs 33 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Modern Data Engineering
2Chapter 1: What Data Engineering Is
3Chapter 2: The Modern Data Engineering Ecosystem
4Chapter 3: Understanding Data Sources
5Chapter 4: Data Ingestion
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6Chapter 5: ETL, ELT, and Pipeline Design
7Chapter 6: Data Storage and File Formats
8Chapter 7: Data Warehousing
9Chapter 8: Data Transformation with SQL
10Chapter 9: Data Modeling for Analytics
11Chapter 10: Workflow Orchestration
12Chapter 11: Real-Time Data and Streaming
13Chapter 12: Data Quality and Testing
14Chapter 13: Governance, Security, and Metadata
15Chapter 14: Cloud Data Engineering
16Introduction:
17The Role of Modern Data Engineering
18What Modern Data Engineering Means
19Why Companies Need Reliable Data Pipelines
20How Data Supports Analytics, AI, Dashboards, and Decision-Making
21What Readers Will Learn from This Book
22How to Use This Book Effectively
23Chapter 1: What Data Engineering Is
24The Role of a Data Engineer
25Data Engineering vs Data Science vs Analytics
26How Data Moves Through an Organization
27Common Responsibilities of Data Engineers
28Skills Needed in Modern Data Engineering
29The Data Engineer’s Mindset
30Chapter 2: The Modern Data Engineering Ecosystem
31The Modern Data Stack
32Databases, Warehouses, Lakes, and Lakehouses
33Databases
34Data Warehouses
35Data Lakes
36Lakehouses
37Comparing the Storage Options
38Batch Systems and Streaming Systems
39Batch Processing
40Streaming Processing
41Choosing Between Batch and Streaming
42Cloud Platforms and Managed Services
43How Tools Work Together in a Data Platform
44Keeping the Ecosystem Simple
45Chapter 3: Understanding Data Sources
46Application Databases
47APIs and Third-Party Services
48Files Such as CSV, JSON, and Parquet
49Logs and Event Data
50SaaS Platforms
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