Training offering

Google Cloud Platform Big Data and Machine Learning Fundamentals


Price £595 before tax
Length: 1 Day
Course code: GOG_GCP-BD
Delivery Type

Session dates

This training is also available as onsite training. Feel free to contact us for more information.


This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, you will get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.


  • Purpose and value of the key Big Data and Machine Learning products in the GoogleCloud Platform

  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL andHadoop/Pig/Spark/Hive workloads to Google Cloud Platform

  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis

  • Train and use a neural network using TensorFlow

  • Employ ML APIs

  • Choose between different data processing products on the Google Cloud Platform


  • Data analysts getting started with Google Cloud Platform

  • Data scientists getting started with Google Cloud Platform

  • Business analysts getting started with Google Cloud Platform

  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports

  • Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists


  • Basic proficiency with common query language such as SQL

  • Experience with data modeling, extract, transform, load activities

  • Developing applications using a common programming language such Python

  • Familiarity with Machine Learning and/or statistics


1. Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview

  • Google Cloud Platform Data Products and Technology

  • Usage scenarios


2. Compute and Storage Fundamentals

  • CPUs on demand (Compute Engine)

  • A global filesystem (Cloud Storage)

  • CloudShell


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


4. Scaling Data Analysis

  • Fast random access

  • Datalab

  • BigQuery

  • Machine Learning with TensorFlow

  • Fully built models for common needs


5. Data Processing Architectures

  • Message-oriented architectures with Pub/Sub

  • Creating pipelines with Dataflow

  • Reference architecture for real-time and batch data processing


6. Summary

  • Why GCP?

  • Where to go from here

  • Additional Resources

Classroom Live Labs

Lab 1: Sign up for Google Cloud Platform

Lab 2: Set up a Ingest-Transform-Publish data processing pipeline

Lab 3: Machine Learning Recommendations with SparkML

Lab 4: Build machine learning dataset

Lab 5: Train and use neural network

Lab 6: Employ ML APIs

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