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Training schedule

IN-COMPANY TRAINING PROGRAMS

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In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

This training is for you if…

you have:

Completed Google Cloud Platform Fundamentals (GO8324) or have equivalent experience
Working knowledge of Node.js
Basic proficiency with command-line tools and Linux operating system environments

This training is not for you if…

you have:

No working knowledge on Node.js
No experience on Linux

Clients we've helped

What you'll learn

Use best practices for application development.
Choose the appropriate data storage option for application data.
Implement federated identity management.
Develop loosely coupled application components or microservices.

The schedule

Module 1: Best Practices for Application Development

Code and environment management
Design and development of secure, scalable, reliable, loosely coupled application components and microservices
Continuous integration and delivery
Re-architecting applications for the cloud

Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK

How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials

Module 3: Overview of Data Storage Options

Overview of options to store application data
Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner

Module 4: Best Practices for Using Cloud Datastore

Best practices related to the following: Queries, Built-in and composite indexes, Inserting and deleting data (batch operations), Transactions, Error handling
Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow
Lab: Store application data in Cloud Datastore

Module 5: Performing Operations on Buckets and Objects

Operations that can be performed on buckets and objects
Consistency model
Error handling

Module 6: Best Practices for Using Cloud Storage

Naming buckets for static websites and other uses
Naming objects (from an access distribution perspective)
Performance considerations
Setting up and debugging a CORS configuration on a bucket
Lab: Store files in Cloud Storage

Module 7: Securing Your Application

Cloud Identity and Access Management (IAM) roles and service accounts
User authentication by using Firebase Authentication
User authentication and authorization by using Cloud Identity-Aware Proxy
Lab: Authenticate users by using Firebase Authentication

Module 8: Using Google Cloud Pub/Sub to Integrate Components of Your Application

Topics, publishers, and subscribers
Pull and push subscriptions
Use cases for Cloud Pub/Sub
Lab: Develop a backend service to process messages in a message queue

Module 9: Adding Intelligence to Your Application

Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API.

Module 10: Using Cloud Functions for Event-Driven Processing

Key concepts such as triggers, background functions, HTTP functions
Use cases
Developing and deploying functions
Logging, error reporting, and monitoring

Module 11: Using Cloud Endpoint to Deploy APIs

Open API deployment configuration
Lab: Deploy an API for your application

Module 12: Debugging Your Application by Using Google Stackdriver

Stackdriver Debugger
Stackdriver Error Reporting
Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting

Module 13: Deploying an Application by Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager

Creating and storing container images
Repeatable deployments with deployment configuration and templates
Lab: Use Deployment Manager to deploy a web application into Google App Engine Flex test and production environments

Module 14: Execution Environments for Your Application

Considerations for choosing an execution environment for your application or service: Google Compute Engine, Container Engine, App Engine Flex, Cloud Functions, Cloud Dataflow
Lab: Deploying your application on App Engine Flex

Module 15: Monitoring and Tuning Performance

Best practices and watchpoints for performance
Key concepts related to Stackdriver Trace and Stackdriver Monitoring
Detecting and resolving performance issues
Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance

Integrate application components and data sources.
Debug, trace, and monitor applications.
Perform repeatable deployments with containers and deployment services.
Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex.

Get In Touch!

Contact Max Driessen now if you want to learn more and take your cloud skills to the next level!

meet your trainer

Ruben Kruiver

Ruben is Cloud Consultant for Xebia. Ruben is completely into tech and a good apatite to learn new things whenever he can. Starting as a professional Software Engineer his carrier lead him through the role of DevOps engineer into the amazing world of Cloud Engineering. From this he has good experience with GCP and is well underway into earning the Professional Cloud Architect. He has already has planned for the next certifications.

Flexible delivery

The Right Format For Your Preferred Learning Style

In-Classroom & In-Company Training

Online, Instructor-Led Training

Hybrid and Blended Learning

Self-Paced Training

Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well

Data scientist

It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.

Technical Leader & Software Architect

I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.

Data Scientist

Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.