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GDI: From Data to Insights with Google Cloud Platform

Code: GDI
Length: 3 Days
Overview

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!

Delivery format(s)
On-Site
On-Site
Classroom
Classroom
OBJECTIVES
This course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: 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 To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL.
WHO SHOULD ATTEND
  • 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 
COURSE OUTLINE

The course includes presentations, demonstrations, and hands-on labs.

Module 1: Introduction to Data on the Google Cloud Platform

  • 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

Module 2: Big Data Tools Overview

  • Walk through 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

Module 3: Exploring your Data with SQL

  • 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

Module 4: Google BigQuery Pricing

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

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
  • Lab: Explore and Shape Data with Cloud Dataprep

Module 6: Storing and Exporting Data

  • Compare Permanent vs Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Creating new Permanent Tables

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
  • Lab: Ingesting and Querying New Datasets

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
  • Lab: Exploring a Dataset in Google Data Studio

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
  • Lab: Join and Union Data from Multiple Tables

Module 10: 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
  • Lab: Deriving Insights with Advanced SQL Functions

Module 11: Schema Design and Nested Data Structures

  • 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

Module 12: More 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: Optimizing for Performance

  • Avoid Google BigQuery Performance Pitfalls
  • Prevent Hotspots in your Data
  • Diagnose Performance Issues with the Query Explanation map
  • Lab: Optimizing and Troubleshooting Query Performance

Module 14: Advanced Insights

  • Introducing Cloud Datalab
  • Cloud Datalab Notebooks and Cells
  • Benefits of Cloud Datalab

Module 15: Data Access

  • Compare IAM and BigQuery Dataset Roles
  • Avoid Access Pitfalls
  • Review Members, Roles, Organizations, Account Administration, and Service Accounts

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