Course Outline

Module 1

Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview.

  • Google Cloud Platform Big Data Products.

Module 2

Compute and Storage Fundamentals

  • CPUs on demand (Compute Engine).

  • A global filesystem (Cloud Storage).

  • CloudShell.

  • Lab: Set up a Ingest-Transform-Publish data processing pipeline. 

Module 3

Data Analytics on the Cloud

  • Stepping-stones to the cloud.

  • Cloud SQL: your SQL database on the cloud.

  • Lab: Importing data into CloudSQL and running queries.

  • Spark on Dataproc.

  • Lab: Machine Learning Recommendations with Spark on Dataproc. 

Module 4

Data Scaling Data Analysis

  • Fast random access.

  • Datalab.

  • BigQuery.

  • Lab: Build machine learning dataset.

Module 5

Machine Learning

  • Machine Learning with TensorFlow.

  • Lab: Carry out ML with TensorFlow

  • Pre-built models for common needs.

  • Lab: Employ ML APIs.

Module 6

Data Processing Architectures

  • Message-oriented architectures with Pub/Sub.

  • Creating pipelines with Dataflow.

  • Reference architecture for real-time and batch data processing. 

Module 7


  • Why GCP?

  • Where to go from here

  • Additional Resources