About this book

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, Data Lakes, Data Warehouse, Pipelines, Orchestration and Looker Studio. Each step is a hands-on learning experience, guided by GitHub-hosted exercises that let you tackle real-world challenges.

This book will guide you into not thinking about ad-hoc implementations but embrace the power of process-driven data engineering. This journey transcends mere solution development; it’s about learning a meticulously crafted process for extracting actionable insights from data. You’ll navigate a structured path, commencing with a discovery phase that unveils the hidden stories within your data and ending with an operational data warehouse for your data analysis.

The tools we cover in this book include:

Python: Construct robust and efficient data pipelines that seamlessly flow through your systems.
Terraform: Build cloud infrastructure with precision, ensuring optimal performance and scalability.
Docker Containers and GitHub Actions CICD: Orchestrate seamless deployments, streamlining your development process and minimizing downtime.
Data Lakes and Data Warehouses: Embrace the complementary strengths of these storage paradigms:
- Data Lakes capture and store vast volumes of raw data.
- Data Warehouses, structured with a Star Schema, is the gateway for the data analysis process.
Pandas and Jupyter Notebooks: Explore and analyze data with flexibility and granular control, fueled by Python’s versatility.
Looker Studio Dashboards: Transform data-driven discoveries into visually compelling narratives that captivate your audience and drive informed decision-making.
Apache Kafka and Apache Spark: Venture into the realm of real-time integration with data-streaming technologies that empower immediate responsiveness and adaptive systems.

By the end, you won’t just be a data enthusiast; you’ll become familiar with the technologies and process to be a data engineer, equipped to conquer similar challenges with confidence. This book isn’t just about learning tools; it’s about understanding the data engineering process. You’ll learn how cloud engineers, DevOps specialists, data analysts, SQL and Python developers weave their work together to transform raw data into actionable insights. This holistic perspective empowers you to collaborate effectively and navigate the data landscape with ease.

👍This book is about HI (Human Intelligence) expertise and experience

Disclaimer

This book provides technical information and code examples based on the author’s knowledge at the time of writing. Technology evolves, and changes may render certain content outdated. Readers are urged to independently verify information and exercise caution when using code, tools, or technologies mentioned. The discussion of third-party tools is for educational purposes; the author does not endorse or guarantee their performance. Code examples are for instructional purposes, with no warranties or guarantees. Readers are responsible for assessing suitability. The content is copyrighted, and readers are permitted to use it within the provided license. However, the author and company are not liable for any consequences or damages arising from the use of this book’s content.