Module 1: Foundations of Data Analytics on Google Cloud
This module sets the stage by introducing the role of BigQuery within the Google Cloud ecosystem and its core architectural concepts.
- Lesson 1: The Modern Data Landscape
- Data Analytics on Google Cloud: An Overview
- From Data to Insights: The Analytics Lifecycle
- Real-World Use Cases: How companies are transformed by cloud analytics
- Lesson 2: Introduction to BigQuery
- What is BigQuery? A Serverless, Petabyte-Scale Data Warehouse
- Core Architecture: Understanding the separation of storage (Colossus) and compute (Dremel)
- Interacting with BigQuery: Console UI, bq command-line tool, and client libraries
Module 2: Core Querying & Data Analysis
Students will get hands-on, learning to explore and analyze data using BigQuery's powerful SQL dialect.
- Lesson 1: Querying Basics
- Writing Your First Query: SELECT, FROM, WHERE, ORDER BY
- Aggregating Data: GROUP BY, COUNT, SUM, AVG
- Filtering Techniques with HAVING
- Lesson 2: Advanced SQL Functions
- Working with Functions: String, date, numeric, and conditional logic (CASE)
- Handling Complex Data Types: STRUCT and ARRAY
- Window Functions: Performing calculations across sets of rows
- Lesson 3: Combining & Structuring Data
- Enriching Queries with JOINs (INNER, LEFT, CROSS)
- Appending Datasets with UNION
- Simplifying Complex Queries with Common Table Expressions (CTEs)
Module 3: Data Ingestion & Transformation
This module focuses on getting data into BigQuery and preparing it for analysis using modern ELT (Extract, Load, Transform) patterns.
- Lesson 1: Ingesting New Datasets
- Batch Loading: Loading data from Cloud Storage (CSV, JSON, Parquet)
- Streaming Ingestion: Working with real-time data flows
- Using External Data Sources and Federated Queries
- Lesson 2: Cleaning & Transforming Data
- 5 Principles of Dataset Integrity
- Cleaning and Transforming Data using SQL (CAST, PARSE, etc.)
- Permanent vs. Temporary Tables for intermediate results
- Lesson 3: Introduction to Dataform
- What is Dataform? Adopting software engineering best practices for SQL
- Getting Started: Building reliable, testable, and documented data transformation pipelines (ELT)
Module 4: Performance, Cost & Security
This crucial module covers the operational aspects of managing a data warehouse effectively and responsibly.
- Lesson 1: BigQuery Performance Optimization
- Understanding the Query Execution Plan
- Best Practices: How to write efficient queries
- Speeding up Queries with Partitioning and Clustering
- Lesson 2: Cost Management & Control
- BigQuery Pricing Explained: On-demand vs. Flat-rate (Slots)
- Estimating Query Costs Before You Run Them
- Setting up Budgets, Quotas, and Alerts
- Lesson 3: Securing Your Data
- Identity and Access Management (IAM) for BigQuery
- Securing Datasets, Tables, and Views
- Advanced Security: Row-level and Column-level security for fine-grained access control
Module 5: Visualization & Advanced Analytics
The final module is about turning analysis into business value through visualization, reporting, and machine learning.
- Lesson 1: Data Visualization
- Key Principles of Effective Data Visualization
- Common Visualization Pitfalls to Avoid
- Lesson 2: Reporting & Dashboards
- Looker Studio: Building interactive dashboards and reports
- Connected Sheets: Analyzing billions of rows of BigQuery data directly in Google Sheets
- Lesson 3: Beyond SQL
- BigQuery ML (BQML): Building and deploying machine learning models (e.g., forecasting, classification) using only SQL
- Analysis in a Notebook: Integrating BigQuery with Vertex AI Notebooks or Colab for advanced exploration
