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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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Serving Data

You have probably heard it said that data is the new oil. What most experts neglect to mention is that data is a costly asset that is only valuable for two things: automating processes and informing your users’ decision-making. In other words, data only becomes useful when you serve it.

So far, we have discussed the multiple stages of the data life cycle (e.g., storage and ingestion). However, the truth is that most of your users do not care about any of that. They do not want to hear about data warehouses, Python packages, or Docker images. If anything, they take all the work necessary to create a bar chart for granted.

This part of the process is your users’ first and only contact with the data. It is the interface between your work and the rest of your organization. So, you want to make a great impression. If you learn how to do it well, you will become invaluable. Every important person in an organization needs data, so knowing how to serve data...

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