Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering

Key Features

• Discover how analytics engineering aligns with your organization's data strategy
• Access insights shared by a team of seven industry experts
• Tackle common analytics engineering problems faced by modern businesses
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Navigate the world of data analytics with Fundamentals of Analytics Engineering—guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights. In this book, you’ll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you’ll also learn how to build a simple data platform using Airbyte for ingestion, DuckDB for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you’ll discover effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment—laying the foundation for consistent and reliable pipelines. And with invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you’ll develop a holistic understanding of the analytics lifecycle. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.

What you will learn

• Design and implement data pipelines from ingestion to serving data
• Explore best practices for data modeling and schema design
• Gain insights into the use of cloud-based analytics platforms and tools for scalable data processing
• Understand the principles of data governance and collaborative coding
• Comprehend data quality management in analytics engineering
• Gain practical skills in using analytics engineering tools to conquer real-world data challenges

Who this book is for

This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.