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
