GCP: Complete Google Data Engineer and Cloud Architect Guide

GCP: Complete Google Data Engineer and Cloud Architect Guide

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What you will learn

  • Deploy Managed Hadoop apps on the Google Cloud
  • Build deep learning models on the cloud using TensorFlow
  • Make informed decisions about Containers, VMs and AppEngine
  • Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub


Section 1: You, This Course and Us

Section 2: Introduction

Section 3: Compute

Section 4: Storage

Section 5: Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS

Section 6: BigTable ~ HBase = Columnar Store

Section 7: Datastore ~ Document Database

Section 8: BigQuery ~ Hive ~ OLAP

Section 9: Dataflow ~ Apache Beam

Section 10: Dataproc ~ Managed Hadoop

Section 11: Pub/Sub for Streaming

Section 12: Datalab ~ Jupyter

Section 13: TensorFlow and Machine Learning

Section 14: Regression in TensorFlow

Section 15: Vision, Translate, NLP and Speech: Trained ML APIs

Section 16: Virtual Machines and Images

Section 17: VPCs and Interconnecting Networks

Section 18: Managed Instance Groups and Load Balancing

Section 19: Ops and Security

Section 20: Appendix: Hadoop Ecosystem

Course Description

The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop


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


This course is a really comprehensive guide to the Google Cloud Platform - it has ~25 hours of content 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.

What's Included:

  • 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)

Who this course is for:

  • 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