In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
This Quest of hands-on labs demonstrates the power of integrating Google Cloud Platform services and tools with G Suite applications. With integration technologies such as App Script and the Clasp Command Line environment, you will create and publish web apps and add-ons for G Suite products: Sheets, Docs, Forms, and Slides. With App Maker you will build a ready-to-use app that has a Google Cloud SQL Database, Google Maps integration, and a Mobile Responsive Design. Other labs create direct connections to GCP data sources-- using the BigQuery API, Sheets, and Slides to collect, analyze and present data.
When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.
In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.
In this Quest, you will delve deeper into the uses and capabilities of Amazon Redshift. You will use a remote SQL client to create and configure tables, and gain practice loading large data sets into Redshift. You will explore the effects of schema variations and compression. You will explore visualization of Redshift data, and connect Redshift with Amazon Machine Learning to create a predictive data model.
It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering it’s development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning APIs by taking labs like Implementing an AI Chatbot with Dialogflow and Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API.
In this fundamental-level quest, you will learn the ins and outs of Stackdriver: an important GCP service for generating insights into applications’ health. Stackdriver provides a wealth of information in application monitoring, report logging, and diagnoses. The labs in this quest will give you hands-on practice with Stackdriver, and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data.
Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.