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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

Data transformation best practices

As seen in previous chapters, analytics engineering embraces software engineering best practices to model, transform, test, deploy, and document data in a reusable way.

When it comes to writing transformation pipelines, SQL is the industry standard. Still, you might also want to use other languages, such as Python or Scala, depending on the tools you use for transformation.

The barrier to entry to writing SQL code is quite low. Thanks to its declarative nature, SQL is easy to read. Most data specialists know how to write SQL, making it easier for organizations to hire talent who can work with SQL pipelines, an important factor in democratizing transformation capabilities.

In this section, we will tackle SQL best practices for your transformation pipelines. We will also mention language specific to dbt and Databricks. In dbt, the SQL files in which developers write SELECT statements are called models. In Databricks, code is organized within...

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