Preface
In today’s data-driven world, the ability to process and analyze vast amounts of data has become a critical competitive advantage for businesses across industries. Big data technologies have emerged as powerful tools to handle the ever-increasing volume, velocity, and variety of data, enabling organizations to extract valuable insights and drive informed decision-making. However, managing and scaling these technologies can be a daunting task, often requiring significant infrastructure and operational overhead.
Enter Kubernetes, the open source container orchestration platform that has revolutionized the way we deploy and manage applications. By providing a standardized and automated approach to container management, Kubernetes has simplified the deployment and scaling of complex applications, including big data workloads. This book aims to bridge the gap between these two powerful technologies, guiding you through the process of implementing a robust and scalable big data architecture on Kubernetes.
Throughout the chapters, you will embark on a comprehensive journey, starting with the fundamentals of containers and Kubernetes architecture. You will learn how to build and deploy Docker images, understand the core components of Kubernetes, and gain hands-on experience in setting up local and cloud-based Kubernetes clusters. This solid foundation will prepare you for the subsequent chapters, where you will dive into the world of the modern data stack.
The book will introduce you to the most widely adopted tools in the big data ecosystem, such as Apache Spark for data processing, Apache Airflow for pipeline orchestration, and Apache Kafka for real-time data ingestion. You will not only learn the theoretical concepts behind these technologies but also gain practical experience in implementing them on Kubernetes. Through a series of hands-on exercises and projects, you will develop a deep understanding of how to build and deploy data pipelines, process large datasets, and orchestrate complex workflows on a Kubernetes cluster.
As the book progresses, you will explore advanced topics such as deploying a data consumption layer with tools such as Trino and Elasticsearch and integrating generative AI workloads using Amazon Bedrock. These topics will equip you with the knowledge and skills necessary to build and maintain a robust and scalable big data architecture on Kubernetes, ensuring efficient data processing, analysis, and analytics application deployment.
By the end of this book, you will have gained a comprehensive understanding of the synergy between big data and Kubernetes, enabling you to leverage the power of these technologies to drive innovation and business growth. Whether you are a data engineer, a DevOps professional, or a technology enthusiast, this book will provide you with the practical knowledge and hands-on experience needed to successfully implement and manage big data workloads on Kubernetes.
