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Fundamentals of Analytics Engineering

You're reading from Fundamentals of Analytics Engineering An introduction to building end-to-end analytics solutions

Product type Paperback

Published in Mar 2024

Publisher Packt

ISBN-13 9781837636457

Length 332 pages

Edition 1st Edition

Tools

dbt

Concepts

Data Analysis

Authors (7):

Dumky De Wilde

Ricardo Angel Granados Lopez

Lasse Benninga

Taís Laurindo Pereira

Jovan Gligorevic

Juan Manuel Perafan

Fanny Kassapian

+3 more

View More author details

Table of Contents (23) Chapters

Preface

1. Prologue

2. Part 1:Introduction to Analytics Engineering FREE CHAPTER

3. Chapter 1: What Is Analytics Engineering?

4. Chapter 2: The Modern Data Stack

5. Part 2: Building Data Pipelines

6. Chapter 3: Data Ingestion

7. Chapter 4: Data Warehousing

8. Chapter 5: Data Modeling

9. Chapter 6: Transforming Data

10. Chapter 7: Serving Data

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

12. Chapter 8: Hands-On Analytics Engineering

13. Part 4: DataOps

14. Chapter 9: Data Quality and Observability

15. Chapter 10: Writing Code in a Team

16. Chapter 11: Automating Workflows

17. Part 5: Data Strategy

18. Chapter 12: Driving Business Adoption

19. Chapter 13: Data Governance

20. Chapter 14: Epilogue

21. Index

22. Other Books You May Enjoy

Digging into the problem of moving data between two systems

We have talked, in Chapter 1, What Is Analytics Engineering?, about the changing process of extracting, transforming, and loading (ETL) data, but understanding these steps is only part of ingesting data. Whenever you add new data to your data platform, whether that is sales data, currency exchange data, web analytics data, or video footage, you will have to make certain choices around the frequency, quality, reliability, and retention of that data and many other choices. If you do not think ahead at the beginning, reality will catch up with you when your data provider makes a change, a pipeline accidentally runs twice, or the requirements from the business change. But why do we need to move data from one system to the other in the first place?

You might consider it a given that you have to manipulate data a bit to make it fit your purpose. Maybe you’re used to pivoting tables in an Excel sheet, adjusting formulas...

The rest of the chapter is locked

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Authors (7)

Dumky De Wilde

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 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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Ricardo Angel Granados Lopez

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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Lasse Benninga

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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Juan Manuel Perafan

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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Gligorevic

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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Kassapian

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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