Certainly! Let’s create a simple example outline for your book using the sections you provided:

Book Outline:

1. Introduction

2. Data Engineering Process Fundamentals Overview

3. Discovery Phase

4. Design and Planning Phase

5. Pipeline Orchestration

6. Data Warehouse Transformation

7. Data Analysis and Visualization

8. Data Streaming

9. Conclusion

10. Appendices

11. Acknowledgments

This is a generalized example, and you can adjust it based on the specific content of your blog entries and the narrative you want to create. The preface serves as an introduction to the entire book, and the conclusion summarizes key points and provides reflections. Each chapter includes an overview and an exercise for hands-on application, fostering an interactive learning experience. The appendices can house additional resources or GitHub links for supporting code.

Feel free to modify this outline according to the unique characteristics of your content and the story you want to tell in your book.

Master the Fundamentals of Data Engineering with a Hands-on Approach

This book transcends theory, thrusting you directly into the dynamic world of data engineering. We’ll journey through the entire process, from discovering raw data to crafting insightful visualizations using powerful tools like Python, Jupyter Notebooks, Terraform, Docker, and Looker Studio. Each step is a hands-on learning experience, guided by GitHub-hosted exercises that let you tackle real-world challenges.

Forget rote memorization; embrace the process. You’ll build data pipelines with Python, sculpt cloud infrastructure with Terraform, and orchestrate deployments with Docker containers. Explore data with Pandas and Jupyter Notebooks, then transform it into compelling stories using Looker Studio dashboards. The journey unfolds like a symphony, with each technology playing its part in the grand performance of data analysis.

By the end, you won’t just be a data enthusiast; you’ll be a data engineer, equipped to conquer any challenge with confidence. Welcome to the code, the cloud, and the captivating world of data.

This book isn’t just about mastering tools; it’s about understanding the interconnectedness of the data engineering process. You’ll learn how cloud engineers, DevOps specialists, data analysts, and SQL developers weave their magic together to transform raw data into actionable insights. This holistic perspective empowers you to collaborate effectively and navigate the data landscape with ease.

Convert to docx

pandoc introduction.md chapter1.md chapter2.md conclusion.md –toc –template my-template.docx -o output.docx

pandoc introduction.md conclusion.md –toc –template template.docx -o output.docx

Use make

make docx

Github reference: