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).
• Global filesystem (Cloud Storage).
• CloudShell.
• Lab: Set up an 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: Scaling Data Analysis

• Fast random access.
• Datalab.
• BigQuery.
• Lab: Build a 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: Summary

• Why GCP?
• Where to go from here
• Additional Resources