GCP: Complete Google Data Engineer and Cloud Architect Guide + Labs

About Course:

This course is a really comprehensive guide to the Google Cloud Platform - it has ~25 hours of content, 84+ hands-on labs and ~60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there - that's AWS of course - but it is possibly the best cloud offering for high-end machine learning applications. That's because TensorFlow, the super-popular deep learning technology is also from Google.

The average salary for a Cloud & Data Engineer Professional is $88,000 per year.

Course Objective:

• Compute and Storage - AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
• Big Data and Managed Hadoop - Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub 
• TensorFlow on the Cloud - what neural networks and deep learning really are, how neurons work and how neural networks are trained.
• DevOps stuff - StackDriver logging, monitoring, cloud deployment manager
• Security - Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
• Networking - Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
• Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)

Audience:

• Yep! Anyone looking to use the Google Cloud Platform in their organizations
• Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
• Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
• Yep! Anyone looking to build TensorFlow models and deploy them on the cloud

Prerequisite:

• Basic understanding of technology - superficial exposure to Hadoop is enough