Course Description

• 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, participants 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.

Objectives

• This course teaches participants the following skills:

• Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
• Use Cloud SQL 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.
• Train and use a neural network using TensorFlow.
• Employ ML APIs.
• Choose between different data processing products on the Google Cloud Platform.
• Interact with Google Cloud Platform services

Audience

This class is intended for the following:

• Data analysts, Data scientists, 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.

Prerequisites

• To get the most of out of this course, participants should have:

• 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 statistic

Content

• The course includes presentations, demonstrations, and hands-on labs.

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).
• Cloud Shell.
• 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 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