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Duration 14 hours
Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Understanding the cost model and pricing framework
- Overview of query execution mechanics and storage solutions
Query Optimization and Cost Management
- Advanced query tuning techniques
- Utilizing partitioned and clustered tables
- Monitoring and analyzing query performance metrics
- Hands-on lab: optimizing queries for maximum cost efficiency
Data Ingestion and Transformation Workflows
- Loading data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- Implementing materialized views and scheduled queries
- Hands-on lab: constructing a comprehensive reporting pipeline
Introduction to BigQuery ML
- Overview of machine learning capabilities within BigQuery
- Supported model types (including linear regression, logistic regression, clustering, etc.)
- SQL syntax for defining ML models
- Hands-on lab: creating and training a machine learning model
Developing Predictive Models with BigQuery ML
- Training and evaluating model accuracy
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Integrating model predictions into business reports
- Hands-on lab: executing a complete predictive analytics workflow
Enterprise Analytics Best Practices
- Governance and access control strategies
- Managing large-scale datasets effectively
- Implementing robust cost control measures
- Case studies demonstrating successful real-world implementations
Summary and Future Directions
Requirements
- Fundamental understanding of SQL
- Familiarity with core data management principles
- Practical experience with reporting or analytics platforms
Intended Audience
- Data analysts
- BI developers
- Data engineers
Testimonials (2)
The final day which is the Machine Learning Topic
John Erick Baltazar - Globe Telecom
Course - Google BigQuery
It was a really good training course, well prepared and explained by the trainer with great hands on experience on GCP.
