This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code.

The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows.

What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world.

What You Will Learn

• Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds
• Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects
• Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure

Who This Book Is For

Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists

Table of Contents

Chapter 1: Core Technologies in Data Engineering

Chapter 2: Data Wrangling using Pandas

Chapter 3: Data Wrangling using Rust's Polars

Chapter 4: GPU Driven Data Wrangling Using CuDF

Chapter 5: Getting Started with Data Validation using Pydantic and Pandera

Chapter 6: Data Validation using Great Expectations

Chapter 7: Introduction to Concurrency Programming and Dask

Chapter 8: Engineering Machine Learning Pipelines using DaskML

Chapter 9: Engineering Real-time Data Pipelines using Apache Kafka

Chapter 10: Engineering Machine Learning and Data REST APIs using FastAPI

Chapter 11: Getting Started with Workflow Management and Orchestration

Chapter 12: Orchestrating Data Engineering Pipelines using Apache Airflow

Chapter 13: Orchestrating Data Engineering Pipelines using Prefect

Chapter 14: Getting Started with Big Data and Cloud Computing

Chapter 15: Engineering Data Pipelines Using Amazon Web Services

Chapter 16: Engineering Data Pipelines Using Google Cloud Platform

Chapter 17: Engineering Data Pipelines Using Microsoft Azure

About the Author

Pavan Kumar Narayanan has an extensive and diverse career in the information technology industry, with a primary focus on the data engineering and machine learning domains. Throughout his professional journey, he has consistently delivered solutions in environments characterized by heterogeneity and complexity. His experience spans a broad spectrum, encompassing traditional data warehousing projects following waterfall methodologies and extending to contemporary integrations that involve APIs and message-based systems. Pavan has made substantial contributions to large-scale data integrations for applications in data science and machine learning. At the forefront of these endeavors, he has played a key role in delivering sophisticated data products and solutions, employing a versatile mix of both traditional and agile approaches. Currently employed with Ether Infinitum LLC, Sheridan, WY, Pavan Kumar Narayanan continues to bring his wealth of experience to the forefront of the data engineering and machine learning landscape.