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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

Understanding a Modern Data Stack

As the name suggests, the MDS represents a technological evolution compared to previous systems widely used in recent decades. From the development of the business data warehouse in the 1980s to the rise of cloud technology with Amazon Web Services (AWS) in the early 2000s, on-premises legacy data stacks dominated the landscape. These systems had a monolithic IT infrastructure, resulting in complex maintenance. The MDS transformed this scenario – bringing modularity and cloud-native tools. However, before we dive into the details, let’s first define what a data stack is.

A data stack is a collection of tools and services as part of an extensive technology infrastructure designed to ingest, store, transform, and serve data. It makes data accessible across an organization and is fundamental to delivering business insights through reporting and dashboards, advanced analytics, and Machine Learning (ML) applications. Figure 2.1 illustrates...

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