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Fundamentals of Analytics Engineering

You're reading from  Fundamentals of Analytics Engineering

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781837636457
Pages 332 pages
Edition 1st Edition
Languages
Authors (7):
Dumky De Wilde Dumky De Wilde
Profile icon Dumky De Wilde
Fanny Kassapian Fanny Kassapian
Profile icon Fanny Kassapian
Jovan Gligorevic Jovan Gligorevic
Profile icon Jovan Gligorevic
Juan Manuel Perafan Juan Manuel Perafan
Profile icon Juan Manuel Perafan
Lasse Benninga Lasse Benninga
Profile icon Lasse Benninga
Ricardo Angel Granados Lopez Ricardo Angel Granados Lopez
Profile icon Ricardo Angel Granados Lopez
Taís Laurindo Pereira Taís Laurindo Pereira
Profile icon Taís Laurindo Pereira
View More author details

Table of Contents (23) Chapters

Preface Prologue
Part 1:Introduction to Analytics Engineering
Chapter 1: What Is Analytics Engineering? Chapter 2: The Modern Data Stack Part 2: Building Data Pipelines
Chapter 3: Data Ingestion Chapter 4: Data Warehousing Chapter 5: Data Modeling Chapter 6: Transforming Data Chapter 7: Serving Data Part 3: Hands-On Guide to Building a Data Platform
Chapter 8: Hands-On Analytics Engineering Part 4: DataOps
Chapter 9: Data Quality and Observability Chapter 10: Writing Code in a Team Chapter 11: Automating Workflows Part 5: Data Strategy
Chapter 12: Driving Business Adoption Chapter 13: Data Governance Chapter 14: Epilogue 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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