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Fundamentals of Analytics Engineering

Fundamentals of Analytics Engineering

By : Dumky De Wilde, Kassapian, Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira
4.7 (3)
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Fundamentals of Analytics Engineering

Fundamentals of Analytics Engineering

4.7 (3)
By: Dumky De Wilde, Kassapian, Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira

Overview of this book

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.
Table of Contents (23 chapters)
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1
Prologue
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Part 1:Introduction to Analytics Engineering
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3
Chapter 1: What Is Analytics Engineering?
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Part 2: Building Data Pipelines
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Chapter 7: Serving Data
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11
Part 3: Hands-On Guide to Building a Data Platform
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Part 4: DataOps
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Part 5: Data Strategy
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Chapter 12: Driving Business Adoption
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Index
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Reviewing the fundamental insights – what you’ve learned so far

In these concluding remarks, we’d like to give you a big-picture view of the topics you have covered so you can see how they connect with each other. This way, you will not only review some key concepts but also learn how to apply them in real-world situations for professional development:

  • Introduction to analytics engineering (Chapters 1-2): The first two chapters introduced the core concepts underpinning analytics engineering and the modern data stack. Remember how analytics engineers leverage software engineering best practices to model, transform, test, deploy, and document data in a reusable way, saving teams time and reducing duplicated effort. On the other hand, the modern data stack optimizes the speed of delivery, costs, and data democratization, but may lead to tool proliferation and siloed information.
  • Building pipelines (Chapters 3-7): These chapters equipped readers with the...

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