Detailed Course Outline
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview
- Google Cloud Platform Data Products and Technology
- Usage scenarios
- Lab: Sign up for Google Cloud Platform
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
- CloudSQL: your SQL database on the cloud
- Lab: Importing data into CloudSQL and running queries
- Spark on Dataproc
- Lab: Machine Learning Recommendations with SparkML
Module 4: Scaling Data Analysis
- Fast random access
- Datalab
- BigQuery
- Lab: Build machine learning dataset
- Machine Learning with TensorFlow
- Lab: Train and use neural network
- Fully built models for common needs
- Lab: Employ ML APIs
Module 5: Data Processing Architectures
- Message-oriented architectures with Pub/Sub
- Creating pipelines with Dataflow
- Reference architecture for real-time and batch data processing
Module 6: Summary
- Why GCP
- Where to go from here
- Additional Resources