Fundamentals of Analytics Engineering

An introduction to building end-to-end analytics solutions

By: Dumky De Wilde, Fanny Kassapian, Jovan Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira and Pádraic Slattery

Paid access

|Sep 2024

E-Book €28.99Institutions €126.95

Description

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

Written by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer.
After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance.
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
Scale data processing with cloud based analytics platforms and tools
Understand the principles of data quality management and data governance
Streamline code base with best practices like collaborative coding, version control, reviews and standards
Automate and orchestrate data pipelines
Drive business adoption with effective scoping and prioritization of analytics use cases

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.