Dataproc Cookbook
Download Dataproc Cookbook PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Dataproc Cookbook book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Dataproc Cookbook
Author: Narasimha Sadineni
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2025-06-03
Want to build big data solutions in Google Cloud? Dataproc Cookbook is your hands-on guide to mastering Dataproc and the essential GCP fundamentals—like networking, security, monitoring, and cost optimization--that apply across Google Cloud services. Learn practical skills that not only fast-track your Dataproc expertise, but also help you succeed with a wide range of GCP technologies. Written by data experts Narasimha Sadineni and Anu Venkataraman, this cookbook tackles real-world use cases like serverless Spark jobs, Kubernetes-native deployments, and cost-optimized data lake workflows. You'll learn how to create ephemeral and persistent Dataproc clusters, run secure data science workloads, implement monitoring solutions, and plan effective migration and optimization strategies. Create Dataproc clusters on Compute Engine and Kubernetes Engine Run data science workloads on Dataproc Execute Spark jobs on Dataproc Serverless Optimize Dataproc clusters to be cost effective and performant Monitor Spark jobs in various ways Orchestrate various workloads and activities Use different methods for migrating data and workloads from existing Hadoop clusters to Dataproc
Google Cloud Cookbook
Author: Rui Santos Costa
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-10-08
Get quick hands-on experience with Google Cloud. This cookbook provides a variety of self-contained recipes that show you how to use Google Cloud services for your enterprise application. Whether you're looking for practical ways to apply microservices, AI, analytics, security, or networking solutions, these recipes take you step-by-step through the process and provide discussions that explain how and why the recipes work. Ideal for system engineers and administrators, developers, network and database administrators, and data analysts, this cookbook helps you get started with Google Cloud regardless of your level of experience. Google veterans Rui Costa and Drew Hodun also cover advanced-level Google Cloud services for those who have appreciable experience with the platform. Learn how to get started with Google Cloud Understand the depth of services Google Cloud provides Gain hands-on experience using practical examples and labs Explore topics that include BigQuery, Cloud Run, and Kubernetes Build and run mobile and web applications on Google Cloud Examine ways to build your cloud applications for scale Build a minimum viable product (MVP) app to use in production Learn data platform and pipeline skills
Java Deep Learning Cookbook
Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch Key FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratchExplore recipes for developing, training, and fine-tuning your neural network models in JavaModel neural networks using datasets containing images, text, and time-series dataBook Description Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently. This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results. By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java. What you will learnPerform data normalization and wrangling using DL4JBuild deep neural networks using DL4JImplement CNNs to solve image classification problemsTrain autoencoders to solve anomaly detection problems using DL4JPerform benchmarking and optimization to improve your model's performanceImplement reinforcement learning for real-world use cases using RL4JLeverage the capabilities of DL4J in distributed systemsWho this book is for If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.