Course 796:
Google Cloud Certification Workshop: Data Engineer

 

Google Cloud Certification Training Description

The workshop is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam.

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

The workshop assumes prior knowledge of Google Cloud Platform (GCP) and is not an introduction to GCP. To see the full Google Cloud Platform 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 Platform. Private events are cost effective, customizable, and convenient. Contact us at googlesales@roitraining.com to find out more.

Learning Objectives

  • Prepare for the GCP Data Engineer certification exam
  • Choose the appropriate GCP data storage solution
  • Architect batch and streaming data processing pipelines on GCP
  • Leverage GCP tools for data manipulation, analysis, and visualization
  • Build machine learning models with GCP tools
  • Analyze case studies to optimize data storage and processing solutions

The workshop includes instructor lecture, 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.

Audience

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 Platform 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 Platform big data services. The exam tests one’s understanding of architecting secure and reliable business solutions that leverage Google Cloud Platform for storing, analyzing, and visualizing data. We strongly recommend taking the Data Engineering on Google Cloud Platform 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 Platform.


Google Cloud Platform Training 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 Platform
  • Architecting Data Processing Solutions on GCP

Module 3: Storing Binary Data

  • Storing Binary Data with Google Cloud Storage
  • Exercise: Google 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: Google Cloud SQL Quickstart
  • Exploiting Spanner for Massively Scalable Relational Systems
  • Exercise: Google Cloud Spanner Quickstart

Module 5: Managed NoSQL Solutions

  • Understanding NoSQL Storage
  • Simplifying Structured Storage with Cloud Datastore
  • Exercise: Google Cloud Datastore Quickstart
  • Storing Massive Data Sets with BigTable
  • Choosing between Datastore and BigTable

Module 6: Big Data Processing and Analytics

  • Migrating Hadoop and Spark Jobs to Google 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 Google Cloud DataFlow
  • Exercise: Google 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: GCP Machine Learning

 Module 10: Case Studies