Managing Real-world Production-grade Challenges at Scale KEY FEATURES ● Built for GCP professionals and Cloud enthusiasts with cloud-agnostic tactics. ● Exhaustive coverage of automatic, manual, and predictive scaling and specialized strategies. ● Every concept is pragmatized with real-time production scenarios derived from prominent technologists. DESCRIPTION ‘Scaling Google Cloud Platform’ equips developers with the know-how to get the most out of its services in storage, serverless computing, networking, infrastructure monitoring, and other IT tasks. This book explains the fundamentals of cloud scaling, including Cloud Elasticity, creating cloud workloads, and selecting the appropriate cloud scaling key performance indicators (KPIs). The book explains the sections of GCP resources that can be scaled, as well as their architecture and internals, and best practices for using these components in an operational setting in detail. The book also discusses scaling techniques such as predictive scaling, auto-scaling, and manual scaling. This book includes real-world examples illustrating how to scale many Google Cloud services, including the compute engine, GKE, VMWare Engine, Cloud Function, Cloud Run, App Engine, BigTable, Spanner, Composer, Dataproc, and Dataflow. At the end of the book, the author delves into the two most common architectures—Microservices and Bigdata to examine how you can perform reliability engineering for them on GCP. WHAT YOU WILL LEARN ● Learn workload migration strategy and execution, both within and between clouds. ● Explore methods of increasing Google Cloud capacity for running VMware Engine and containerized applications. ● Scaling up and down methods include manual, predictive, and automatic approaches. ● Increase the capacity of your Dataproc cluster to handle your big data computing needs. ● Learn Google Dataflow's scalability considerations for large-scale installations. ● Explore Google Composer 2 and scale up your Cloud Spanner instances. ● Learn to set up Cloud functions and Cloud run. ● Discuss general SRE procedures on microservices and big data. WHO THIS BOOK IS FOR This book is designed for Cloud professionals, software developers, architects, DevOps team, and engineering managers to explain scaling strategies for GCP services and assumes readers know GCP basics. TABLE OF CONTENTS 1. Basics of Scaling Cloud Resources 2. KPI for Cloud Scalability 3. Cloud Elasticity 4. Challenges of Infrastructure Complexity and the Way Forward 5. Scaling Compute Engine 6. Scaling Kubernetes Engine 7. Scaling VMware Engine 8. Scaling App Engine 9. Scaling Google Cloud Function and Cloud Run 10. Configuring Bigtable for Scale 11. Configuring Cloud Spanner for Scale 12. Scaling Google Composer 2 13. Scaling Google Dataproc 14. Scaling Google Dataflow 15. Site Reliability Engineering 16. SRE Use Cases

Develop, deploy, and scale your applications with Google Cloud Platform Key Features Create and deploy your applications on Google Cloud Platform Store and manage source code and debug Cloud-hosted apps with plugins and IDEs Streamline developer workflows with tools for alerting and managing deployments Book Description Google Cloud Platform (GCP) provides autoscaling compute power and distributed in-memory cache, task queues, and datastores to write, build, and deploy Cloud-hosted applications. With Google Cloud Platform for Developers, you will be able to develop and deploy scalable applications from scratch and make them globally available in almost any language. This book will guide you in designing, deploying, and managing applications running on Google Cloud. You’ll start with App Engine and move on to work with Container Engine, compute engine, and cloud functions. You’ll learn how to integrate your new applications with the various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. This book will teach you how to streamline your workflow with tools such as Source Repositories, Container Builder, and StackDriver. Along the way, you’ll see how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerting for your production systems. By the end of this book, you’ll be well-versed with all the development tools of Google Cloud Platform, and you’ll develop, deploy, and manage highly scalable and reliable applications. What you will learn Understand the various service offerings on GCP Deploy and run services on managed platforms such as App Engine and Container Engine Securely maintain application states with Cloud Storage, Datastore, and Bigtable Leverage StackDriver monitoring and debugging to minimize downtime and mitigate issues without impacting users Design and implement complex software solutions utilizing Google Cloud Integrate with best-in-class big data solutions such as Bigquery, Dataflow, and Pub/Sub Who this book is for Google Cloud Platform for Developers is for application developers. This book will enable you to fully leverage the power of Google Cloud Platform to build resilient and intelligent software solutions.

Explore the Essential Concepts, Tools, and Services in GCP KEY FEATURES ● Build a solid foundation of the Google Cloud Platform. ● Work with different AI and Machine Learning services offered by Google Cloud. ● Learn how to use Google cloud services to build scalable apps. DESCRIPTION Google Cloud platform has a suite of cloud computing services for developing and maintaining software. It includes products like Google Compute Engine, Google App Engine, Google Cloud Storage, and Google Container Engine. With so much to offer, we will learn how to manage services running on Google Cloud. ‘Google Cloud Platform All-In-One Guide’ is primarily for everyone who wants to get familiar with the comprehensive list of services in GCP. You will work with various cloud-based services in computing, storage, database, and networking domains. You will understand how Big Data services can be used for developing end-to-end ETL/ELT pipelines. Lastly, you will explore various APIs available in Google cloud. The book ends with a chapter on best practices that will help you maximize resource utilization and cost optimization. By the end of the book, you will be able to design, develop, and deploy apps in GCP. WHAT YOU WILL LEARN ● Explore and work with security and monitoring services in Google Cloud. ● Learn how to build an ETL Pipeline in the Google Cloud Platform. ● Build and deploy code-based custom models using Vertex AI and Jupyter notebook. ● Learn how to create workflows using GCP services. ● Get an overview of best practices for securely deploying your workloads on Google Cloud. WHO THIS BOOK IS FOR This book is for everyone new to cloud computing or Google cloud. Cloud professionals who are looking to migrate their services to the Google cloud platform will find this book helpful. TABLE OF CONTENTS 1. Cloud Computing Fundamentals 2. Compute in Google Cloud 3. Storage in Google Cloud 4. Database Services in Google Cloud 5. Networking in Google Cloud 6. Security and Monitoring Services in Google Cloud 7. Big Data in Google Cloud 8. AI/ML in Google Cloud 9. Orchestration Services in GCP 10. Migration Services in GCP 11. Best Practices 12. Bonus Chapter 13. Use Cases

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Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

Build cost-effective and robust cloud solutions with Google Cloud Platform (GCP) using these simple and practical recipes Key FeaturesExplore the various service offerings of the GCPHost a Python application on Google Compute EngineSecurely maintain application states with Cloud Storage, Datastore, and BigtableBook Description GCP is a cloud computing platform with a wide range of products and services that enable you to build and deploy cloud-hosted applications. This Learning Path will guide you in using GCP and designing, deploying, and managing applications on Google Cloud. You will get started by learning how to use App Engine to access Google's scalable hosting and build software that runs on this framework. With the help of Google Compute Engine, you’ll be able to host your workload on virtual machine instances. The later chapters will help you to explore ways to implement authentication and security, Cloud APIs, and command-line and deployment management. As you hone your skills, you’ll understand how to integrate your new applications with various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. Following this, the book will teach you how to streamline your workflow with tools, including Source Repositories, Container Builder, and Stackdriver. You'll also understand how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerts for your production systems. By the end of this Learning Path, you'll be well versed with GCP’s development tools and be able to develop, deploy, and manage highly scalable and reliable applications. This Learning Path includes content from the following Packt products: Google Cloud Platform for Developers Ted Hunter and Steven PorterGoogle Cloud Platform Cookbook by Legorie Rajan PSWhat you will learnHost an application using Google Cloud FunctionsMigrate a MySQL database to Cloud SpannerConfigure a network for a highly available application on GCPLearn simple image processing using Storage and Cloud FunctionsAutomate security checks using Policy ScannerDeploy and run services on App Engine and Container EngineMinimize downtime and mitigate issues with Stackdriver Monitoring and DebuggerIntegrate with big data solutions, including BigQuery, Dataflow, and Pub/SubWho this book is for This Learning Path is for IT professionals, engineers, and developers who want to implement Google Cloud in their organizations. Administrators and architects planning to make their organization more efficient with Google Cloud will also find this Learning Path useful. Basic understanding of GCP and its services is a must.

Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable.