Google Cloud Platform Big Data And Machine Learning Fundamentals (CPB100) Bootcamp

In this course, you will learn about the big data and machine learning capabilities of the Google Cloud Platform. You will be provided with a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

For a more general overview of Google Cloud Platform, see Google Cloud Platform Fundamentals (CP100A).


  • Purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
  • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis
  • Choose between Cloud SQL, BigTable, and Datastore
  • Train and use a neural network using TensorFlow
  • Choose between different data processing products on the Google Cloud Platform

Target Audience

  • Data analysts
  • Data scientists
  • Business analysts


At least one year of experience with one or more of the following:
  • A common query language such as SQL?
  • Extract, transform, load activities
  • Data modeling
  • Machine learning and/or statistics
  • Programming in Python


Expected Duration

1 day

Course Objectives

1. Introduction

  • What is the Google Cloud Platform?
  • GCP Big Data Products
  • Usage scenarios
  • Sign up for Google Cloud Platform

2. Foundation of GCP (Compute and Storage) æ

  • CPUs on demand (Compute Engine)
  • A global filesystem (Cloud Storage)æ
  • CloudShellæ

3. Data Analytics on the Cloud

  • Stepping stones to the cloud
  • CloudSQL: Your SQL database on the cloud
  • Dataproc

4. Scaling Data Analysis

  • Fast random access
  • Datalabæ
  • BigQuery
  • Machine Learning with TensorFlowæ
  • Fully built models for common needsæ
  • Genomics API

5. Data Processing Architectures æ

  • Asynchronous processing with TaskQueuesæ
  • Message-oriented architectures with Pub/Sub
  • Creating pipelines with Dataflowæ

6. Summary æ

  • Why GCP?æ
  • Where to go from hereæ
  • Resources