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

Google BigQuery


While data processing engines such as Cloud Dataflow and Hadoop offer extreme computational power, they do so by following a well-defined execution plan, often with long delays in converting new data into usable insights. For many analytics workflows, this turnaround time is critical. As an example, suppose a marketing executive needs to know the effectiveness of recent changes to a marketing campaign for a given set of regions and a given demographic. Also suppose that the size of data involved is in the order of terabytes. These answers could certainly be determined using the likes of MapReduce or Dataflow, but doing so would involve developing, testing, and validating a new pipeline. If the results prompt further questions, the entire iteration cycle must start again.

For many tasks like this, a more ad-hoc and interactive approach is ideal, and data warehouse solutions have long been the go-to answer. Internally, Google has long used their home-grown analytical database...