BigQuery for Analytics and Machine Learning
Learn to architect scalable data pipelines, optimize storage with partitioning, and deploy machine learning models directly within the BigQuery warehouse environment.
intermediate level · 12h · 7 modules
- Architecture and Data Ingestion
- BigQuery Storage Fundamentals
- External Tables and Data Loading
- Nested and Repeated Fields
- Data Schema Best Practices
- Querying Complex Data Structures
- Optimization and Storage Costs
- Partitioning Strategies
- Clustering for Performance
- Cost Optimization Techniques
- Query Execution Plan
- Partitioning and Clustering Implementation
- BigQuery Machine Learning
- BQML Overview
- Linear and Logistic Regression
- Evaluating BQML Models
- Feature Store Fundamentals
- Training an ML Model
- Advanced Analytics Workflow
- SQL Window Functions
- Materialized Views
- Data Security and IAM
- Scheduled Queries
- Automating Data Pipelines
- Integration and Production Scaling
- Connecting Looker Studio
- BigQuery Dataframes
- Vertex AI Integration
- End-to-End Pipeline Scaling
- BigQuery Analytics Pipeline Build
- Predictive Feature Pipeline Challenge
