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Duration 14 hours
Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost models and pricing structures
- Overview of query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for tuning queries
- Implementation of partitioned and clustered tables
- Monitoring and analyzing query performance
- Hands-on lab: enhancing query cost efficiency
Data Ingestion and Transformation
- Importing data from external sources
- Utilizing Dataflow and Dataprep for ETL processes
- Implementing materialized views and scheduled queries
- Hands-on lab: constructing a reporting pipeline
Getting Started with BigQuery ML
- Overview of machine learning capabilities in BigQuery
- Supported model types (e.g., linear regression, logistic regression, clustering)
- SQL syntax for defining ML models
- Hands-on lab: creating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating models
- Using ML.EVALUATE and ML.PREDICT functions
- Integrating predictions into reporting workflows
- Hands-on lab: executing a predictive analytics workflow
Best Practices for Enterprise Analytics
- Governance and access control strategies
- Managing large datasets at scale
- Strategies for cost control
- Case studies of successful implementations
Summary and Next Steps
Requirements
- Fundamental understanding of SQL
- Knowledge of data management principles
- Practical experience with reporting or analytics tools
Target 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.
