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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
Free Chapter
2
Part 1:Introduction to Analytics Engineering
5
Part 2: Building Data Pipelines
11
Part 3: Hands-On Guide to Building a Data Platform
13
Part 4: DataOps
17
Part 5: Data Strategy
21
Index

Summary

Data governance refers to any task you must do to make your data compliant, secure, accurate, available, and useful. Even though organizations often ignore it, it sets mature data teams apart. It enables you to work towards your strategic goals and reduce the hours wasted maintaining and fixing existing data assets.

In this chapter, we discuss some key topics in governance, such as ownership, data quality, managing data assets, training, and data modeling. A recurrent theme is that building governance roadmaps from scratch is generally not your responsibility. However, analytics engineers are in a privileged position to understand issues with the data and have enough technical knowledge to correct them at the source.

Working on data governance is never going to be easy. You will face resistance to change and need to get buy-in from your stakeholders to ensure the success of your initiatives. However, any goal you achieve will translate into a much better experience for...

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