Course 796:
Google Cloud Advanced Skills & Certification Workshop: Professional Data Engineer

 

Google Cloud Certification Training Description

This workshop is designed to help IT professionals prepare for the Google Cloud Certified Professional Data Engineer exam.

In this workshop, we review the exam guidelines and product strategies for the major Cloud Storage, big data, and analytics services covered by the exam. We examine concepts related to data transformation, real-time processing, visualization, machine learning, and best practices to solve common problems.

This workshop assumes prior knowledge of Google Cloud and is not an introduction to Google Cloud. To see the full Google Cloud curriculum, click here.

Dedicated Onsite Training for Your Team 

Have a team of people you are looking to certify? ROI Training will send a Google Authorized Trainer to you and work with you to customize a certification track based on your experience with Google Cloud. Private events are cost-effective, customizable, and convenient. Contact us at googlesales@roitraining.com to find out more.

Learning Objectives

  • Prepare for the Google Cloud Professional Data Engineer certification exam
  • Choose the appropriate Google Cloud data storage solution
  • Store binary, relational, and NoSQL data using Google Cloud services
  • Secure data using IAM and encryption
  • Architect batch and streaming data processing pipelines on Google Cloud
  • Leverage Google Cloud tools for data manipulation, analysis, and visualization
  • Build machine learning models with Google Cloud tools

This workshop includes instructor lectures, group activities, case study discussions, practice exams, and links to recommended study, videos, and tutorials. Homework assignments are also included to help students further prepare for the exam.

Who Should Attend

IT professionals interested in obtaining the Google Certified Professional Data Engineer certification. Data scientists and machine learning practitioners who want to learn more about taking optimal advantage of the big data services provided by Google Cloud will also benefit from this course.

Prerequisites

Prior to taking the Google Cloud Data Engineer Professional exam, students should have prior experience working with Google Cloud big data services. The exam tests one’s understanding of architecting secure and reliable business solutions that leverage Google Cloud for storing, analyzing, and visualizing data. We strongly recommend taking the Data Engineering on Google Cloud course prior to attending this workshop.

Practice Quizzes and Case Study Examples

Included with this course are sample quizzes and numerous case study examples that will help you both prepare for the exam, and have a greater level of understanding of how to build data analytics and machine learning systems on Google Cloud.


Course Outline

Module 1: Data Engineer Certification Overview

Module 2: Google Big Data Fundamentals

  • Google Big Data History and Overview
  • Choosing the Right Storage Option
  • Securing Your Data on Google Cloud
  • Architecting Data Processing Solutions on Google Cloud

Module 3: Storing Binary Data

  • Storing Binary Data with Cloud Storage
  • Exercise: Cloud Storage
  • Understanding Persistent Disks Storage
  • Exercise: Disks and Snapshots

Module 4: Storing Relational Data  

  • Modeling Relational Data
  • Moving Relational Databases to Cloud SQL
  • Exercise: Cloud SQL Quickstart
  • Exploiting Spanner for Massively Scalable Relational Systems
  • Exercise: Cloud Spanner Quickstart

Module 5: Managed NoSQL Solutions

  • Understanding NoSQL Storage
  • Simplifying Structured Storage with Cloud Firestore and Datastore
  • Exercise: Cloud Datastore/Firestore Quickstart
  • Storing Massive Data Sets with Bigtable
  • Choosing between Firestore and Bigtable
  • Caching Data using Memorystore

Module 6: Big Data Processing and Analytics

  • Migrating Hadoop and Spark Jobs to Cloud Dataproc
  • Exercise: Creating Dataproc Clusters
  • Big Data Warehousing and Analytics with BigQuery
  • Denormalizing Data for Query Optimization in BigQuery
  • Exercise: Querying Data with BigQuery
  • Choosing Big Data Processing Strategies

Module 7: Data Processing Pipelines

  • Programming ETL Pipelines with Cloud Dataflow
  • Simplify Dataflow Coding Using Templates
  • Exercise: Cloud Dataflow
  • Designing Real-time Data Processing Systems
  • Leveraging Pub/Sub for Scalable, Asynchronous Messaging
  • Preparing Data for Analysis with Cloud DataPrep

Module 8: Visualization and Analytics

  • Manipulating and Analyzing Data with Cloud Datalab
  • Building Dashboards with Data Studio

Module 9: Machine Learning Fundamentals

  • Machine Learning Use Cases and Algorithms
  • Training and Evaluating Models
  • Feature Engineering
  • Analyzing Machine Learning Case Studies
  • Programming Models with TensorFlow
  • Exercise: Getting Started with TensorFlow
  • Serverless, NoOps Training with Google Cloud MLE
  • Exercise: Google Cloud Machine Learning
  • Automating Machine Learning with AutoML and BigQuery ML