Book Image

Building Google Cloud Platform Solutions

By : Ted Hunter, Steven Porter, Legorie Rajan PS
Book Image

Building Google Cloud Platform Solutions

By: Ted Hunter, Steven Porter, Legorie Rajan PS

Overview of this book

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 Porter • Google Cloud Platform Cookbook by Legorie Rajan PS
Table of Contents (29 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Cloud Dataflow


Google Cloud Dataflow is a managed data transformation service, with a unified data processing model designed to process both unbounded and bounded datasets. Cloud Dataflow is a serverless platform—developers write code in the form of pipelines, and submit those pipelines to Cloud Dataflow for execution. There are no servers or other infrastructure to manage, allowing teams to quickly get up and running with large-scale data transformations. The core design of Cloud Dataflow allows for advanced concepts, such as autoscaling workers and dynamically rebalancing workloads across those workers, greatly lowering execution time while maximizing efficiency.

With integrations across the Google Cloud Platform catalog, Cloud Dataflow is a very flexible service and can handle data processing needs for a wide array of use cases. This includes both traditional data analytics workloads, as well as common operational tasks, such as database migrations, replaying Pub/Sub messages stored in...