From Data to Insights with Google Cloud Platform

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

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Learning Objectives

This course teaches students the following skills:

  • 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


    Course Details

    Course Outline

    1 - Introduction to Data on the Google Cloud Platform
  • Before and Now: Scalable Data Analysis in the Cloud
  • Topics Covered
  • Highlight Analytics Challenges Faced by Data Analysts
  • Compare Big Data On-Premises vs on the Cloud
  • Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
  • Navigate Google Cloud Platform Project Basics
  • Lab: Getting started with Google Cloud Platform
  • 2 - Big Data Tools Overview
  • Sharpen the Tools in your Data Analyst toolkit
  • Topics Covered
  • 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
  • Lab: Exploring Datasets with Google BigQuery
  • 3 - Exploring your Data with SQL
  • Get Familiar with Google BigQuery and Learn SQL Best Practices
  • Topics Covered
  • Compare Common Data Exploration Techniques
  • Learn How to Code High Quality Standard SQL
  • Explore Google BigQuery Public Datasets
  • Visualization Preview: Google Data Studio
  • Lab: Troubleshoot Common SQL Errors
  • 4 - Google BigQuery Pricing
  • Calculate Google BigQuery Storage and Query Costs
  • Topics Covered
  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimize Queries for Cost
  • Lab: Calculate Google BigQuery Pricing
  • 5 - Cleaning and Transforming your Data
  • Wrangle your Raw Data into a Cleaner and Richer Dataset
  • Topics Covered
  • 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
  • Lab: Explore and Shape Data with Cloud Dataprep
  • 6 - Storing and Exporting Data
  • Create new Tables and Exporting Results
  • Topics Covered
  • Compare Permanent vs Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Creating new Permanent Tables
  • 7 - Ingesting New Datasets into Google BigQuery
  • Bring your Data into the Cloud
  • Topics Covered
  • Query from External Data Sources
  • Avoid Data Ingesting Pitfalls
  • Ingest New Data into Permanent Tables
  • Discuss Streaming Inserts
  • Lab: Ingesting and Querying New Datasets
  • 8 - Data Visualization
  • Effectively Explore and Explain your Data through Visualization
  • Topics Covered
  • Overview of Data Visualization Principles
  • Exploratory vs Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
  • Lab: Exploring a Dataset in Google Data Studio
  • 9 - Joining and Merging Datasets
  • Combine and Enrich your Datasets with more Data
  • Topics Covered
  • 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
  • Lab: Join and Union Data from Multiple Tables
  • 10 - Advanced Functions and Clauses
  • Dive Deeper into Advanced Query Writing with Google BigQuery
  • Topics Covered
  • 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
  • Lab: Deriving Insights with Advanced SQL Functions
  • 11 - Schema Design and Nested Data Structures
  • Model your Datasets for Scale in Google BigQuery
  • Topics Covered
  • Compare Google BigQuery vs Traditional RDBMS Data Architecture
  • Normalization vs Denormalization: Performance Tradeoffs
  • Schema Review: The Good, The Bad, and The Ugly
  • Arrays and Nested Data in Google BigQuery
  • Lab: Querying Nested and Repeated Data
  • 12 - More Visualization with Google Data Studio
  • Create Pixel-Perfect Dashboards
  • Topics Covered
  • Create Case Statements and Calculated Fields
  • Avoid Performance Pitfalls with Cache considerations
  • Share Dashboards and Discuss Data Access considerations
  • 13 - Optimizing for Performance
  • Troubleshoot and Solve Query Performance Problems
  • Topics Covered
  • Avoid Google BigQuery Performance Pitfalls
  • Prevent Hotspots in your Data
  • Diagnose Performance Issues with the Query Explanation map
  • Lab: Optimizing and Troubleshooting Query Performance
  • 14 - Advanced Insights
  • Think, Analyze, and Share Insights like a Data Scientist
  • Topics Covered
  • Introducing Cloud Datalab
  • Cloud Datalab Notebooks and Cells
  • Benefits of Cloud Datalab
  • 15 - Data Access
  • Keep Data Security top-of-mind in the Cloud
  • Topics Covered
  • Compare IAM and BigQuery Dataset Roles
  • Avoid Access Pitfalls
  • Review Members, Roles, Organizations, Account Administration, and Service Accounts
  • Actual course outline may vary depending on offering center. Contact your sales representative for more information.

    Who is it For?

    Target Audience

    This course is intended for the following:

    Data Analysts, Business Analysts, Business Intelligence professionals

    Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform

    Other Prerequisites

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

    Basic proficiency with ANSI SQL

    From Data to Insights with Google Cloud Platform

    Course Length : 2 Days (16 Hours)

    There are currently no scheduled dates for this course. Please contact us for more information.

    Need Help Picking the Right Course? Give us a call! 800-201-0555