Discover how analytics engineering aligns with your organization's data strategy
Access insights shared by a team of seven industry experts
Tackle common analytics engineering problems faced by modern businesses
Purchase of the print or Kindle book includes a free PDF eBook

Description

Navigate the world of data analytics with Fundamentals of Analytics Engineering—guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights. You’ll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you’ll also learn how to build a simple data platform using Airbyte for ingestion, Google BigQuery for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you’ll find effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment—laying the foundation for consistent and reliable pipelines. With invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you’ll develop a holistic understanding of the analytics lifecycle. 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.

What you will learn

Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Gain insights into the use of cloud-based analytics platforms and tools for scalable data processing Understand the principles of data governance and collaborative coding Comprehend data quality management in analytics engineering Gain practical skills in using analytics engineering tools to conquer real-world data challenges

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Product Details

Publication date : Mar 29, 2024

Length 332 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781837636457

Category :

Data

Concepts :

Data Analysis

23 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Conventions used

Get in touch

Share Your Thoughts

Prologue

Part 1:Introduction to Analytics Engineering

Chapter 1: What Is Analytics Engineering?

Introducing analytics engineering

Why do we need analytics engineering?

Summary

Chapter 2: The Modern Data Stack

Understanding a Modern Data Stack

Explaining three key differentiators versus legacy stacks

Discussing the advantages and disadvantages of the MDS

Summary

Part 2: Building Data Pipelines

Chapter 3: Data Ingestion

Digging into the problem of moving data between two systems

Understanding the eight essential steps of a data ingestion pipeline

Managing the quality and scalability of data ingestion pipelines – the three key topics

Working with data ingestion – an example pipeline

Summary

Chapter 4: Data Warehousing

Uncovering the evolution of data warehousing

Moving to the cloud

Building blocks of a cloud data warehouse

Knowing the market leaders in cloud data warehousing

Summary

Chapter 5: Data Modeling

The importance of data models

Designing your data model

Data modeling techniques

Choosing a data model

Summary

Chapter 6: Transforming Data

Transforming data – the foundation of analytics work

Design choices

Data transformation best practices

Tools that facilitate data transformations

Summary

Chapter 7: Serving Data

Exposing data using dashboarding and BI tools

Serving data – four key topics

Summary

Part 3: Hands-On Guide to Building a Data Platform

Chapter 8: Hands-On Analytics Engineering

Technical requirements

Understanding the Stroopwafelshop use case

Preparing Google Cloud

Modeling data using dbt Cloud

Visualizing data with Tableau

Selecting the KPIs

Summary

Part 4: DataOps

Chapter 9: Data Quality and Observability

Understanding the problem of data quality at the source, in transformations, and in data governance

Finding solutions to data quality issues – observability, data catalogs, and semantic layers

Summary

Chapter 10: Writing Code in a Team

Identifying the responsibilities of team members

Tracking tasks and issues

Managing versions with version control

Working with coding standards

Reviewing code

Continuous integration/continuous deployment

Documenting code

Working with containers

Summary

Chapter 11: Automating Workflows

Introducing DataOps

Orchestrating data pipelines

Continuous integration

Continuous deployment

Continuous delivery

Summary

Part 5: Data Strategy

Chapter 12: Driving Business Adoption

Defining analytics translation

Scoping analytics use cases

Ensuring business adoption

Summary

Chapter 13: Data Governance

Understanding data governance

Applying data governance in analytics engineering

Addressing critical areas for seamless data governance

Summary

Chapter 14: Epilogue

Reviewing the fundamental insights – what you’ve learned so far

Making your career future-proof – how to take it further

Closing remarks

Index

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Authors (7)

Fanny Kassapian

Fanny has a multidisciplinary background across various industries, giving her a unique perspective on analytics workflows, from engineering pipelines to driving value for the business. As a consultant, Fanny helps companies translate opportunities and business needs into technical solutions, implement analytics engineering best practices to streamline their pipelines, and treat data as a product. She is an avid promoter of data democratization, through technology and literacy

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Ricardo Angel Granados Lopez

Ricardo, an Analytics Engineer with a strong background in data engineering and analysis, is a quick learner and tech enthusiast. With a Master's in IT Management specializing in Data Science, he excels in using various programming languages and tools to deliver valuable insights. Ricardo, experienced in diverse industries like energy, transport, and fintech, is adept at finding alternative solutions for optimal results. As an Analytics Engineer, he focuses on driving value from data through efficient data modeling, using best practices, automating tasks and improving data quality

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Dumky De Wilde

Dumky is an award-winning analytics engineer with close to 10 years of experience in setting up data pipelines, data models and cloud infrastructure. Dumky has worked with a multitude of clients from government to fintech and retail. His background is in marketing analytics and web tracking implementations, but he has since branched out to include other areas and deliver value from data and analytics across the entire organization.

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Taís Laurindo Pereira

Taís is a versatile data professional with experience in a diverse range of organizations - from big corporations to scale-ups. Before her move to Xebia, she had the chance to develop distinct data products, such as dashboards and machine learning implementations. Currently, she has been focusing on end-to-end analytics as an Analytics Engineer. With a mixed background in engineering and business, her mission is to contribute to data democratization in organizations, by helping them to overcome challenges when working with data at scale

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Jovan Gligorevic

Jovan, an Analytics Engineer, specializes in data modeling and building analytical dashboards. Passionate about delivering end-to-end analytics solutions and enabling self-service analytics, he has a background in business and data science. With skills ranging from machine learning to dashboarding, Jovan has democratized data across diverse industries. Proficient in various tools and programming languages, he has extensive experience with the modern data stack. Jovan enjoys providing trainings in dbt and Power BI, sharing his knowledge generously

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Juan Manuel Perafan

Juan Manuel Perafan 8 years of experience in the realm of analytics (5 years as a consultant). Juan was the first analytics engineer hired by Xebia back in 2020. Making him one of the earliest adopters of this way of working. Besides helping his clients realizing the value of their data, Juan is also very active in the data community. He has spoken at dozens of conferences and meetups around the world (including Coalesce 2023). Additionally, he is the founder of the Analytics Engineering meetup in the Netherlands as well as the Dutch dbt meetup

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Lasse Benninga

Lasse has been working in the dataspace since 2018, starting out as a Data Engineer at a large airline, then switching towards Cloud Engineering for a consultancy and working for different clients in the retailing and healthcare space. Since 2021, he's an Analytics Engineer at Xebia Data, merging software/platform engineering with analytics passion. As a consultant Lasse has seen many different clients, ranging from retail, healthcare, ridesharing industry, and trading companies. He has implemented multiple data platforms and worked in all three major clouds, leveraging his knowledge of data and analytics to provide value

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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions

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