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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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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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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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Data modeling techniques

During the 1990s, as data warehouses rapidly integrated into numerous enterprises and businesses, Bill Inmon, Ralph Kimball, and Daniel Linstedt developed methodologies with architectures for constructing data warehouses.

Each of these methods has its own associated data model. While other methods or possible combinations exist, the three main forms discussed in the following subsections are the most common.

Bill Inmon and relational modeling

In 1992, William (Bill) Inmon published Building the Data Warehouse (https://www.wiley.com/en-us/Building+the+Data+Warehouse,+4th+Edition-p-9780764599446). For this work, he is recognized as one of the fathers of the data warehouse.

Inmon’s methodology for data warehouse architecture highlights the importance of a unified data storage system structured according to the third normal form (3NF), which will be described in depth in the next section. He asserts that robust relational modeling contributes...

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