Data Engineering Crash Course

AI is becoming the backbone of industrial solutions; applications that help in the prediction of the life of a machine, face detection, text generation, and image generation are the results of great algorithms that are at work on huge datasets. However, these algorithms work on a simple concept, such as garbage in, garbage out; that is, if the data is not right, the algorithm will fail to give the expected results.

This crash course book outlines the practical issues that are faced by the industry while collecting data and how these problems can be solved using advanced tools that we have in the current market. This book shows the step-by-step implementation of how the filtering of the data can be done, what logic can be applied to get the best output of the target algorithm with the current dataset, and the book will also cover how the data is fed into the pipeline where it can be used for further processing and predicting the outcomes.

By the end of the book, readers will be prepared to align their thought process, starting from taking the basic steps to select the correct data, putting it into the right structure, optimizing processing and engineering on the same so that the right algorithm can be trained on this data to give the best results that will be in line with the expectation of the end customer.

WHAT YOU WILL LEARN
● Understand core principles across the entire data engineering lifecycle.
● Analyze technical architectures essential for modern data projects.
● Process data using exploratory analysis and feature engineering.
● Build automated ingestion pipelines to feed target systems.
● Apply foundational statistical methods for analytical decision support.
● Utilize machine learning tools alongside cloud computing frameworks.
● Implement governance frameworks following strict global security laws.

WHO THIS BOOK IS FOR
The book is ideal for students, aspiring data engineers, software developers, database administrators, and IT professionals transitioning into machine learning. Readers should possess basic Python programming, foundational SQL querying skills, and familiarity with general computer science concepts like data structures and file systems.

1. Basic Understanding of Data Engineering
2. Designing a Data Architecture
3. Data Generation in Source System
4. Data Ingestion
5. Data Transformation
6. Statistics in Data Engineering
7. Introduction to Data Storage
8. Practical Guide to Data Storage
9. Introduction to Machine Learning
10. Understanding Cloud Computing
11. Mapping Technologies in Data Engineering Lifecycle
12. Key Tools Powering Modern Data Engineering
13. Understanding Stream Analytics
14. Data Governance and Security
15. Core Concepts of Business Analytics
16. Business Analytics Implementation and Problem Solving

Vipin Naudiyal is a seasoned technology leader with over 16 years of experience in software development, automation, and digital transformation. Currently, he is working as the director of digital transformation in one of the leading business process management organizations. He leads GenAI-driven projects and architects enterprise-scale solutions across diverse industries including BFSI, travel, shipping, and logistics.


He has worked with leading organizations like Schneider Electric, General Electric, and Emerson Electric, and has driven the development of several innovative technology solutions.


Vipin holds a postgraduate diploma in data engineering from IIT Jodhpur, and in this book, he shares practical insights to help aspiring data engineers build real-world, job-ready skills.