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Fundamentals-of-Analytics-Engineering

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"Fundamentals of Analytics Engineering, published by Packt"

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# Fundamentals of Analytics Engineering

This is the code repository for [Fundamentals of Analytics Engineering](https://www.packtpub.com/product/fundamentals-of-analytics-engineering/9781837636457), published by Packt.

**An introduction to building end-to-end analytics solutions**

## What is this book about?
Boost your analytics engineering journey with this comprehensive guide, written by 7 industry experts. This book is an indispensable resource that arms you with all the knowledge, skills, and insights you need to excel in this rapidly evolving field.

This book covers the following exciting features:
* 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

If you feel this book is for you, get your [copy](https://www.amazon.com/Fundamentals-Analytics-Engineering-end-end/dp/1837636451/ref=sr_1_1?crid=13OF41ZQ3A70O&dib=eyJ2IjoiMSJ9.JtztU9fstRnYFLxR1zhIJy64ezLDM41ag2KxTr3fERTwiVEVtp5RI2WOFN4JVX7SwT3zLOIXA3tB9HYG1WmUSttfZ6Ycbu6rc3cQ2XnkF2vL5GNo62OXKVJUlglJFiRIDNo87XveMUCFs-n2WmW65tCA1IvB6GscgYy7rmeJcoaWk23YYinEAto-jJsHyXs4TLrQuSQkEk-Uw4Dym8WXlkpVjJ7OuckJ_3KYe19TIpM.iC478iIxKX3AsbHhLpqsUMEiKS4PG7vUHx6r3w29Uyk&dib_tag=se&keywords=Fundamentals+of+Analytics+Engineering&qid=1713188556&sprefix=fundamentals+of+analytics+engineering%2Caps%2C335&sr=8-1) today!
<a href="https://www.packtpub.com/?utm_source=github&utm_medium=banner&utm_campaign=GitHubBanner"><img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" 
alt="https://www.packtpub.com/" border="5" /></a>
## Instructions and Navigations
The repository is organized in chapters. In chapter 8 **Hands-on Analytics Engineering**, there are references to code and guides for setting up Google Cloud, Google BigQuery, Airbye Cloud, and dbt Cloud. The code and the guides are placed in the `chapter_8` directory.

Several chapters in the book feature code snippets designed to showcase best practices. However, executing these snippets may need extra setup, not covered in this book. These snippets are not stored in this repository, or anywhere else. Readers should view these snippets as illustrative examples and adapt the underlying best practices to their unique scenarios.

The code will look like the following:
```
def add_numbers(a, b):
    c = a + b
    return c
```

**Following is what you need for this book:**
This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.

With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
## Software and Hardware List
| Chapter | Software required | OS required |
| -------- | ------------------------------------ | ----------------------------------- |
| 1-14 | Airbyte Cloud | WindowsOS, macOS, or Linux |
| 1-14 | dbt Cloud | WindowsOS, macOS, or Linux |
| 1-14 | Google Cloud, Google BigQuery Google Sheets | WindowsOS, macOS, or Linux |
| 1-14 | Tableau Desktop  | WindowsOS, macOS, or Linux |
| 1-14 | Git and GitHub |  WindowsOS, macOS, or Linux |

To get the most out of this repository, it's recommended to read the book and use the code and guides in this repository to follow along. Each guide provides step-by-step instructions to implement the concepts discussed in

## Getting Started
1. Clone this repository to your local machine.
2. Follow along in the book, and refer to this repository when the book references code or guides on GitHub.

## Related products
* Data Engineering with dbt [[Packt]](https://www.packtpub.com/product/data-engineering-with-dbt/9781803246284) [[Amazon]](https://www.amazon.com/Data-Engineering-dbt-cloud-based-dependable/dp/1803246286/ref=sr_1_1?crid=2SNL3QPR0PKAP&dib=eyJ2IjoiMSJ9.ymZC7by8vDEUpw0oRZFLQZVQkbZHOmvYdkTkpnKP3sF2bEV-DMN6O5FYv7H-GFyrtIKUg4PMx-8iO_uLgXGidvmYykYE2trGpDfEi_4GRwNDcm0_5pzdRiB2QRSLBHdMBBog3k8hyTKQzYor2jCfiY2tlnar8MZ8rAJNgcI33MEtnvdVfUZUEVRyp5_QO0amVR9ZlyKc7ygtTKMeni305s3nefEITa1N6IeMn10iIgI.5HZzayQbAIM_bAuSYO-wdm8Dmflt3MuVYx0-7VyBwVY&dib_tag=se&keywords=Data+Engineering+with+dbt&qid=1713189084&sprefix=data+engineering+with+dbt%2Caps%2C720&sr=8-1)

* Data Engineering with Python [[Packt]](https://www.packtpub.com/product/data-engineering-with-python/9781839214189) [[Amazon]](https://www.amazon.com/Data-Engineering-Python-datasets-pipelines-ebook/dp/B08DSLVFNR/ref=sr_1_1?crid=195QVX8M9DM5D&dib=eyJ2IjoiMSJ9.YwuIbS3Lv07h-fwiu9dE4mz4eixkwuqx8SQ3FnkALRKK69xr0tXJJS7tBo5esDT9FmfEpY-xrspI4kG0hwAoxFhK1INl0ElaAjdg8fbR0Qd7J5ElGGFASHhi2Bks-oiuPb7ERS228n0xEVOd2FqshXRE7yFBWQWQaEcIf1fR7sEG6zxO534c3GAfA-ew2QaKgpb0JtMbwWHca7QkXdwdQ16y5ro8T_MuiSol5wMfHUY.QFP23Y5a-Yp6DAFxK8JfSfPv8OQZet3YZHsJYlpDcIg&dib_tag=se&keywords=Data+Engineering+with+Python&qid=1713189152&sprefix=data+engineering+with+python%2Caps%2C351&sr=8-1)

## Get to Know the Authors
**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

**Fanny Kassapian**
 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

**Ricardo Angel Granados Lopez**
, 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

**Jovan Gligorevic**
, 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

**Taís Laurindo Pereira**
 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

**Lasse Benninga**
 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

**Dumky De Wilde,**
 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

## Contribute to the GitHub Repository
We welcome contributions! If you find a bug or think something could be improved, please open an issue. We appreciate your help in improving this companion repository.

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