Big Data on Kubernetes (eBook)

In 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.

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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 FeaturesLeverage Kubernetes in a cloud environment to integrate seamlessly with a variety of toolsExplore best practices for optimizing the performance of big data pipelinesBuild end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and KafkaPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnInstall and use Docker to run containers and build concise imagesGain a deep understanding of Kubernetes architecture and its componentsDeploy and manage Kubernetes clusters on different cloud platformsImplement and manage data pipelines using Apache Spark and Apache AirflowDeploy and configure Apache Kafka for real-time data ingestion and processingBuild and orchestrate a complete big data pipeline using open-source toolsDeploy Generative AI applications on a Kubernetes-based architectureWho 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. ]]>