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Duration 14 hours
Course Outline
Getting Started with BigQuery
- BigQuery architecture and key features
- Overview of query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for query tuning
- Implementation of partitioned and clustered tables
- Monitoring and analyzing query performance
- Practical lab: optimizing queries for maximum cost efficiency
Data Ingestion and Transformation Processes
- Importing data from external sources
- Utilizing Dataflow and Dataprep for ETL tasks
- Application of materialized views and scheduled queries
- Practical lab: constructing a complete reporting pipeline
Introduction to BigQuery ML
- Supported model types (including linear regression, logistic regression, and clustering)
- SQL syntax for defining ML models
- Practical lab: creating and training a model
Developing Predictive Models with BigQuery ML
- Processes for training and evaluating models
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Incorporating predictions into reports
- Practical lab: end-to-end predictive analytics workflow
Best Practices for Enterprise-Level Analytics
- Handling large datasets at scale
- Strategies for cost control
- Case studies highlighting successful implementations
Conclusion and Recommended Next Steps
Requirements
- Foundational understanding of SQL
- Experience with reporting or analytical tools
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.
