Complete Course of Google BigQuery
Google BigQuery is a fully-managed, serverless data warehouse designed for handling large-scale data analytics with lightning-fast query performance.
It allows users to run SQL queries on massive datasets stored in the cloud, making it an essential tool for big data analytics, business intelligence, and machine learning tasks.
BigQuery's scalability and ease of integration with other Google Cloud services make it a popular choice for organizations looking to analyze and derive insights from large data sets in real-time.
Learning Google BigQuery is important for big data professionals, as it enables them to manage and analyze big data efficiently without worrying about infrastructure management.
Chapter 1: Introduction to Big Data and Cloud Data Warehousing
Lesson 1: Understanding Big Data: Concepts, Challenges, and Opportunities
Lesson 2: Overview of Big Data Ecosystems: From On-Premises Systems to Cloud Platforms
Lesson 3: Cloud Data Warehousing vs. Traditional Big Data Frameworks
Lesson 4: Comparing Big Data Tools (e.g., Apache Hadoop, Spark) with Google BigQuery
Lesson 5: Real-World Use Cases for Cloud-Based Analytics
Chapter 2: Introduction to Google BigQuery
Lesson 1: What is Google BigQuery?: An Overview
Lesson 2: History and Evolution of BigQuery in the Google Cloud Ecosystem
Lesson 3: Key Features of BigQuery: Serverless Architecture, Scalability, and Real-Time Analytics
Lesson 4: Understanding BigQuery’s Underlying Technologies (Dremel, Columnar Storage)
Lesson 5: Advantages, Limitations, and When to Use BigQuery
Chapter 3: Getting Started with BigQuery: Setup and Environment
Lesson 1: Creating and Configuring Your Google Cloud Account for BigQuery
Lesson 2: Navigating the BigQuery Web UI and Cloud Console
Lesson 3: Introduction to the BigQuery Integrated Development Environment (IDE)
Lesson 4: Installing and Using the BigQuery Command-Line Tool (bq CLI)
Lesson 5: Setting Up API Access and Client Libraries (Python, Java, etc.)
Chapter 4: BigQuery Data Storage and Management
Lesson 1: Core Storage Concepts: Projects, Datasets, and Tables
Lesson 2: Schema Design and Data Modeling in BigQuery
Lesson 3: Table Partitioning and Clustering: Concepts and Best Practices
Lesson 4: Data Loading Techniques: Using UI, bq Commands (e.g., bq load), and Streaming Inserts
Lesson 5: Data Export, Backup Strategies, and Managing Data Lifecycle
Lesson 6: Hands-on: Creating and Managing Datasets with Sample Commands
Chapter 5: Querying Data with BigQuery
Lesson 1: Introduction to BigQuery SQL: Standard SQL vs. Legacy SQL
Lesson 2: Writing Basic Queries: SELECT, FROM, WHERE, GROUP BY, ORDER BY
Lesson 3: Advanced Query Techniques: Joins, Subqueries, Window Functions, and Arrays/Structs
Lesson 4: Data Manipulation with DML Commands: INSERT, UPDATE, DELETE
Lesson 5: Creating and Managing Views and Materialized Views
Lesson 6: Using User-Defined Functions (UDFs) for Custom Processing
Lesson 7: Best Practices for Writing Efficient and Cost-Effective Queries
Chapter 6: BigQuery Commands and Technical Tools
Lesson 1: Overview of the bq Command-Line Tool and Its Syntax
Lesson 2: Key bq Commands: Query, Load, Extract, and Show
Lesson 3: Automating Tasks with bq Scripting and Scheduling
Lesson 4: Integrating BigQuery with the Cloud SDK and API
Lesson 5: Real-World Command Examples and Troubleshooting Query Failures
Lesson 6: Analyzing Query Execution Plans and Performance Insights
Chapter 7: BigQuery Security and Administration
Lesson 1: Access Control and IAM Roles in BigQuery
Lesson 2: Data Encryption and Security Best Practices
Lesson 3: Configuring Audit Logs and Monitoring Access
Lesson 4: Implementing Data Governance and Compliance Standards
Lesson 5: Managing Permissions for Projects, Datasets, and Tables
Lesson 6: Securing BigQuery in a Multi-Tenant Environment
Chapter 8: Performance Optimization and Cost Management
Lesson 1: Understanding Query Execution and Cost Implications
Lesson 2: Techniques for Query Optimization: Partitioning, Clustering, and Materialized Views
Lesson 3: Monitoring and Analyzing Query Performance with Google Cloud’s Tools
Lesson 4: Cost Optimization Strategies: Controlling Data Scanned and Query Resources
Lesson 5: Hands-on Lab: Optimizing a Complex Query and Reducing Costs
Lesson 6: Best Practices for Data Modeling and Query Design
Chapter 9: Advanced BigQuery Features and Extensions
Lesson 1: Introduction to BigQuery ML: Building and Deploying Machine Learning Models
Lesson 2: Leveraging BigQuery GIS for Geospatial Analytics
Lesson 3: BigQuery Scripting and Stored Procedures for Complex Workflows
Lesson 4: User-Defined Functions (UDFs): Advanced Usage and Performance Considerations
Lesson 5: Exploring BigQuery’s Integration with R and Python for Data Science
Chapter 10: Integrating BigQuery with the Broader Big Data Ecosystem
Lesson 1: Using BigQuery with Dataflow for ETL and Streaming Data
Lesson 2: Integrating BigQuery with Apache Beam for Real-Time Processing
Lesson 3: Connecting BigQuery to BI Tools (Google Data Studio, Looker, Tableau)
Lesson 4: BigQuery and Google Cloud Storage: Data Ingestion and Export
Lesson 5: Leveraging BigQuery with Apache Spark on Google Cloud Dataproc
Lesson 6: Integrations with Third-Party Tools and Custom Connectors
Chapter 11: BigQuery Administration and Operational Management
Lesson 1: Project and Billing Management in BigQuery
Lesson 2: Configuring Reservations, Slots, and Quotas for Workload Management
Lesson 3: Monitoring, Logging, and Alerts with Cloud Monitoring Tools
Lesson 4: Scheduling Queries and Managing Batch vs. Interactive Workloads
Lesson 5: Maintenance, Upgrades, and Backup/Recovery Strategies
Lesson 6: Hands-on: Setting Up a Managed BigQuery Environment
Chapter 12: New Features and Updates in BigQuery Releases
Lesson 1: Overview of Recent and Upcoming BigQuery Enhancements
Lesson 2: New SQL and Query Engine Features: What’s Changed?
Lesson 3: Enhancements in BigQuery Storage API and Data Streaming Capabilities
Lesson 4: BigQuery Omni: Cross-Cloud Data Analysis and Its Use Cases
Lesson 5: Improved Support for Semi-Structured Data (JSON, Avro, Parquet)
Lesson 6: Latest Innovations in Security, Performance, and Cost Management
Chapter 13: Real-World Applications of BigQuery
Lesson 1: BigQuery for Real-Time Analytics and Reporting
Lesson 2: Using BigQuery in E-Commerce, Marketing, and Financial Services
Lesson 3: BigQuery in IoT and Telemetry Data Analysis
Lesson 4: Case Studies: Implementing BigQuery in Healthcare, Genomics, and Smart Cities
Lesson 5: Future Trends and Emerging Use Cases in Cloud Data Warehousing
Chapter 14: Troubleshooting and Debugging BigQuery
Lesson 1: Common Issues and Error Messages in BigQuery
Lesson 2: Debugging Queries and Analyzing Job Failures
Lesson 3: Tools and Techniques for Troubleshooting Performance Bottlenecks
Lesson 4: Best Practices for Maintaining Data Integrity
Lesson 5: Community Resources and Google Support Channels
Chapter 15: Best Practices and Advanced Use Cases
Lesson 1: Writing Efficient and Maintainable BigQuery Code
Lesson 2: Data Governance, Compliance, and Audit Best Practices
Lesson 3: Cost Management Strategies and Optimization Techniques
Lesson 4: Advanced Data Modeling and Query Design Patterns
Lesson 5: Preparing for BigQuery’s Next Evolution and Future Challenges
Chapter 16: BigQuery Certification and Career Pathways
Lesson 1: Overview of Google Cloud Certifications Involving BigQuery
Lesson 2: Exam Preparation: Key Topics and Study Resources
Lesson 3: Building a Portfolio with BigQuery Projects
Lesson 4: Career Opportunities in Cloud Data Warehousing and Analytics
Lesson 5: Community Engagement: Forums, Meetups, and Continued Learning
