Big Data On Kubernetes
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Big Data on Kubernetes
Author: Neylson Crepalde
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-07-19
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 for If 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.
Kubernetes for Data Engineers: Orchestrating Big Data and AI Pipelines 2025
Author: Author:1- KARAN SINGH ALANG, Author:1- Dr RUPESH MISHRA
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE In today’s rapidly evolving world of data engineering, the need for scalable, efficient, and reliable infrastructure has never been more critical. With the advent of big data, artificial intelligence (AI), and machine learning (ML), the complexity of managing and deploying sophisticated data pipelines has grown exponentially. Enter Kubernetes, the open-source platform that has redefined how applications are deployed, scaled, and managed across a distributed environment. Kubernetes for Data Engineers: Orchestrating Big Data and AI Pipelines is written for data engineers, architects, and technologists who seek to leverage the power of Kubernetes in the realm of data processing and AI/ML workflows. This book serves as a practical guide for mastering the skills necessary to efficiently manage large-scale data workloads, while also offering insights into Kubernetes’ core features and its application to data-intensive tasks. Throughout this book, we explore how Kubernetes can help streamline the deployment, management, and scaling of big data technologies and AI/ML pipelines, enabling you to manage diverse tools like Hadoop, Spark, TensorFlow, and more, all within a Kubernetes environment. By adopting Kubernetes’ orchestration and automation capabilities, data engineers can drive performance, reduce overhead, and ensure resilience across the data processing lifecycle. In addition to covering fundamental Kubernetes concepts, we will also dive deep into the specific challenges faced by data engineers and how Kubernetes addresses them. From managing containerized services for distributed systems to automating data pipelines, this book will walk you through hands-on examples, case studies, and best practices to ensure you can effectively apply these concepts in your own projects. As data engineering becomes more intricate and interwoven with AI-driven innovations, the demand for Kubernetes skills will continue to rise. Whether you are already familiar with Kubernetes or just beginning to
Big Data
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