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Full Description
Gain hands-on experience in building efficient and scalable big data architecture on Kubernetes, utilizing leading technologies such as Spark, Airflow, Kafka, and Trino
Key Features
Leverage Kubernetes in a cloud environment to integrate seamlessly with a variety of tools
Explore best practices for optimizing the performance of big data pipelines
Build end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and Kafka
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionIn today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data. Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you.
Written by an experienced data specialist, Big Data on Kubernetes takes you through the entire process of developing scalable and resilient data pipelines, with a focus on practical implementation. Starting with the basics, you'll progress toward learning how to install Docker and run your first containerized applications. You'll then explore Kubernetes architecture and understand its core components. This knowledge will pave the way for exploring a variety of essential tools for big data processing such as Apache Spark and Apache Airflow. You'll also learn how to install and configure these tools on Kubernetes clusters. Throughout the book, you'll gain hands-on experience building a complete big data stack on Kubernetes.
By the end of this Kubernetes book, you'll be equipped with the skills and knowledge you need to tackle real-world big data challenges with confidence.What you will learn
Install and use Docker to run containers and build concise images
Gain a deep understanding of Kubernetes architecture and its components
Deploy and manage Kubernetes clusters on different cloud platforms
Implement and manage data pipelines using Apache Spark and Apache Airflow
Deploy and configure Apache Kafka for real-time data ingestion and processing
Build and orchestrate a complete big data pipeline using open-source tools
Deploy Generative AI applications on a Kubernetes-based architecture
Who this book is forIf you're a data engineer, BI analyst, data team leader, data architect, or tech manager with a basic understanding of big data technologies, then this big data book is for you. Familiarity with the basics of Python programming, SQL queries, and YAML is required to understand the topics discussed in this book.
Contents
Table of Contents
Getting Started with Containers
Kubernetes Architecture
Kubernetes - Hands On
The Modern Data Stack
Big Data Processing with Apache Spark
Apache Airflow for Building Pipelines
Apache Kafka for Real-Time Events and Data Ingestion
Deploying the Big Data Stack on Kubernetes
Data Consumption Layer
Building a Big Data Pipeline on Kubernetes
AI/ML Workloads on Kubernetes
Where to Go from Here
