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Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost models and pricing structures
- Overview of query execution and storage mechanisms
Query Optimisation and Cost Reduction
- Techniques for tuning queries
- Utilisation of partitioned and clustered tables
- Monitoring and analysing query performance metrics
- Practical lab: optimising queries for cost-effectiveness
Data Ingestion and Transformation
- Loading data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- Implementation of materialized views and scheduled queries
- Practical lab: constructing a reporting pipeline
Introduction to BigQuery ML
- Overview of machine learning capabilities within BigQuery
- Supported model types (linear regression, logistic regression, clustering, etc.)
- SQL syntax for defining ML models
- Practical lab: creating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating model performance
- Application of ML.EVALUATE and ML.PREDICT functions
- Integrating predictions into reporting structures
- Practical lab: executing a predictive analytics workflow
Best Practices for Enterprise Analytics
- Governance frameworks and access control
- Managing large-scale datasets
- Strategies for cost management
- Case studies of successful deployments
Summary and Future Directions
Requirements
- Foundational proficiency in SQL
- Understanding of data management principles
- Prior experience with reporting or analytics platforms
Target Audience
- Data analysts
- BI developers
- Data engineers
14 Hours
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.
