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IL - Data to Insights with Google Cloud Platform
Instructor-Led Training
Intermediate
2 Days
Onsite or Virtual
Course Overview
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.
Objectives
  • 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
Pre-Requisites
  • Basic proficiency with ANSI SQL

Module 1: Introduction to Data on the Google Cloud Platform

  • 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

  • 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

  • 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

  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimize Queries for Cost

Module 5: Cleaning and Transforming your Data

  • 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

  • Compare Permanent vs. Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache

Module 7: Ingesting New Datasets into Google BigQuery

  • Query from External Data Sources
  • Avoid Data Ingesting Pitfalls
  • Ingest New Data into Permanent Tables
  • Discuss Streaming Inserts

Module 8: Data 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

  • 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

  • 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

  • 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 Case Statements and Calculated Fields
  • Avoid Performance Pitfalls with Cache Considerations
  • Share Dashboards and Discuss Data Access Considerations

Module 13: Advanced Functions and Clauses

  • 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

  • Avoid Google BigQuery Performance Pitfalls
  • Prevent Hotspots in Data
  • Diagnose Performance Issues with the Query Explanation Map

Module 15: Advanced Insights

  • Distill Complex Queries
  • Brainstorm Data-Driven Hypotheses
  • Think like a Data Scientist
  • Introducing Cloud Datalab

Module 16: Data Access

  • Compare IAM and BigQuery Dataset Roles
  • Avoid Access Pitfalls
  • Review Members, Roles, Organizations, Account Administration, and Service Accounts
Dedicated Training
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Dedicated instructor-led training is designed for group training and is delivered by the experts at Opsgility. Delivery availability is anywhere in the world at your location or using advanced virtual training software.

Benefits
  • Standard or Customized Curriculum
  • Globally Available for Delivery
  • Holistic Learning Plans are Available
  • Industry Recognized Subject Matter Experts