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

Uploading data to the Google BigQuery table


Google BigQuery is a petabyte scale, serverless, low cost data analytics service. The ability to use standard SQL is one of the great advantages from a developer standpoint. BigQuery uses columnar storage, massively parallel processing, and performance adjustments for the data processing of large datasets.

In this recipe, we'll learn to insert data into a BigQuery table continuously and later query it from the web interface.

Getting ready

The following steps are the initial setup verification steps for the creation of the network before the recipe can be executed:

  1. Create or select a GCP project
  2. Enable billing and enable the default APIs (some APIs like BigQuery, storage, monitoring, and a few others are enabled automatically)

How to do it...

In this recipe, we'll extract some data from Twitter using the Twitter API and load it into a BigQuery table. Once the data is loaded, we'll use the web interface to query the stored items:

  1. Navigate to the BigQuery...