• Course
  • Vendor

Use the Google Cloud Platform for data analysis and visualization.

  • Course Start Date: 2019-06-26
  • Time: 10:00:00 - 19:00:00
  • Duration: 2 days 10:00 AM - 07:00 PM
  • Location: Virtual
  • Delivery Methods(s): Virtual Instructor Led

Course Outline


Basic proficiency with ANSI SQL


Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course!

This two-day, instructor-led course teaches participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.

Derive insights from data using the analysis and visualization tools on Google Cloud Platform
Interactively query datasets using Google BigQuery
Load, clean, and transform data at scale
Visualize data using Google Data Studio and other third-party platforms
Distinguish between exploratory and explanatory analytics and when to use each approach
Explore new datasets and uncover hidden insights quickly and effectively
Optimizing data models and queries for price and performance

Module 1: Introduction to Data on the Google Cloud Platform
Before and Now: Scalable Data Analysis in the Cloud
  • Highlight Analytics Challenges Faced by Data Analysts
  • Compare Big Data On-Premise vs. on the Cloud
  • Learn from Real-World Use Cases of Companies Transformed Through Analytics on the Cloud
  • Navigate Google Cloud Platform Project Basics
Module 2: Big Data Tools Overview
Sharpen the Tools in your Data Analyst toolkit
  • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
  • Demo: Analyze 10 Billion Records with Google BigQuery
  • Explore 9 Fundamental Google BigQuery Features
  • Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
Module 3: Exploring your Data
Get Familiar with Google BigQuery and Learn SQL Best Practices
  • Compare Common Data Exploration Techniques
  • Learn How to Code High Quality Standard SQL
  • Explore Google BigQuery Public Datasets
  • Visualization Preview: Google Data Studio
Module 4: Google BigQuery Pricing
Calculate Google BigQuery Storage and Query Costs
  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimize Queries for Cost
Module 5: Cleaning and Transforming your Data
Wrangle your Raw Data into a Cleaner and Richer Dataset
  • Examine the 5 Principles of Dataset Integrity
  • Characterize Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Clean and Transform Data using a new UI: Introducing Cloud Dataprep
Module 6: Storing and Exporting Data
Create new Tables and Exporting Results
  • Compare Permanent vs. Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
Module 7: Ingesting New Datasets into Google BigQuery
Bring your Data into the Cloud
  • Query from External Data Sources
  • Avoid Data Ingesting Pitfalls
  • Ingest New Data into Permanent Tables
  • Discuss Streaming Inserts
Module 8: Data Visualization
Effectively Explore and Explain Data through Visualization
  • Overview of Data Visualization Principles
  • Exploratory vs. Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
Module 9: Joining and Merging Datasets
Combine and Enrich Datasets with More Data
  • Merge Historical Data Tables with UNION
  • Introduce Table Wildcards for Easy Merges
  • Review Data Schemas: Linking Data Across Multiple Tables
  • Walkthrough JOIN Examples and Pitfalls
Module 10: Google BigQuery Tables Deep Dive
What Sets Cloud Architecture Apart?
  • Compare Data Warehouse Storage Methods
  • Deep-Dive into Column-Oriented Storage
  • Examine Logical Views, Date-Partitioned Tables, and Best Practices
  • Query the Past with Time Travelling Snapshots
Module 11: Schema Design and Nested Data Structures
Model Datasets for Scale in Google BigQuery
  • Compare Google BigQuery vs. Traditional RDBMS Data Architecture
  • Normalization vs. Denormalization: Performance Trade-Offs
  • Schema Review: The Good, The Bad, and The Ugly
  • Arrays and Nested Data in Google BigQuery
Module 12: Advanced Visualization with Google Data Studio
Create Pixel-Perfect Dashboards
  • Create Case Statements and Calculated Fields
  • Avoid Performance Pitfalls with Cache Considerations
  • Share Dashboards and Discuss Data Access Considerations
Module 13: Advanced Functions and Clauses
Dive Deeper into Advanced Query Writing with Google BigQuery
  • Review SQL Case Statements
  • Introduce Analytical Window Functions
  • Safeguard Data with One-Way Field Encryption
  • Discuss Effective Sub-query and CTE design
  • Compare SQL and Javascript UDFs
Module 14: Optimizing for Performance
Troubleshoot and Solve Query Performance Problems
  • Avoid Google BigQuery Performance Pitfalls
  • Prevent Hotspots in Data
  • Diagnose Performance Issues with the Query Explanation Map
Module 15: Advanced Insights
Think, Analyze, and Share Insights Like a Data Scientist
  • Distill Complex Queries
  • Brainstorm Data-Driven Hypotheses
  • Think like a Data Scientist
  • Introducing Cloud Datalab
Module 16: Data Access
Keep Data Security Top-of-Mind in the Cloud
  • Compare IAM and BigQuery Dataset Roles
  • Avoid Access Pitfalls
  • Review Members, Roles, Organizations, Account Administration, and Service Accounts

Lab: Getting Started with Google Cloud Platform
Lab: Exploring Datasets with Google BigQuery
Lab: Troubleshoot Common SQL Errors
Lab: Calculate Google BigQuery Pricing
Lab: Explore and Shape Data with Cloud Dataprep
Lab: Creating New Permanent Tables
Lab: Ingesting and Querying New Datasets
Lab: Exploring a Dataset in Google Data Studio
Lab: Join and Union Data from Multiple Tables
Lab: Querying Nested and Repeated Data
Lab: Visualizing Insights with Google Data Studio
Lab: Deriving Insights with Advanced SQL Functions
Lab: Optimizing and Troubleshooting Query Performance
Lab: Reading a Google Cloud Datalab Notebook

Cancellation Policy

We require 16 calendar days notice to reschedule or cancel any registration. Failure to provide the required notification will result in 100% charge of the course. If a student does not attend a scheduled course without prior notification it will result in full forfeiture of the funds and no reschedule will be allowed. Within the required notification period, only student substitutions will be permitted. Reschedules are permitted at anytime with 16 or more calendar days notice. Enrollments must be rescheduled within six months of the cancel date or funds on account will be forfeited.

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About Global Knowledge


Global Knowledge is the world's leading learning services and professional development solutions provider. We deliver learning solutions to support customers as they adapt to key business transformations and technological advancements that drive the way that organizations around the world differentiate themselves and thrive. Our learning programs, whether designed for a global organization or an individual professional, help businesses close skills gaps and foster an environment of continuous talent development.

Training Provider Rating

This vendor has an overall average rating of 4.39 out of 5 based on 424 reviews.

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Wasn’t as advanced as I thought it would be. There was an issue when the day my course was the first time they used a new platfo ... Read more

Wasn’t as advanced as I thought it would be. There was an issue when the day my course was the first time they used a new platform.. from adobe to something called zoom; I had to call support line cause it stated our instructor wasn’t present. Thankfully I called cause everyone online was in the adobe virtual classroom waiting for what looked like a teacher who didn’t show up for class (IT didn’t get anything resolved until 10mins after start time). I felt like he was really getting hung up on very basic knowledge for the first half of the course (talking about how to create tabs and drag formulas as an example). I completed files a few times before he was done explaining. There was a scheduled fire drill for them (roughly 30mins)that also cut into our time, which wasn’t deducted from the hour lunch break or the two, fifteen min breaks. I also really wish he touched base more on the automating workbook functions portion which we barely did. I'm happy there were/are those study guides (learning videos) and exams to take on my own time that I hope after I've had the class are still available for me to learn from.

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It was difficult to practice on my PC while trying to watch the presentation online.
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David was excellent!! I am very for having this course!!

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