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Duration 14 hours
Course Outline
Getting Started with BigQuery
- Understanding BigQuery architecture and core features
- Exploring the cost model and pricing framework
- Overview of query execution mechanics and storage systems
Query Optimization and Cost Reduction
- Techniques for tuning queries
- Utilizing partitioned and clustered tables
- Monitoring and assessing query performance
- Practical lab: enhancing query cost efficiency
Data Ingestion and Transformation Processes
- Importing data from external sources
- Employing Dataflow and Dataprep for ETL tasks
- Implementing 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 (including linear regression, logistic regression, and clustering)
- SQL syntax for defining ML models
- Practical lab: generating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating models
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Integrating predictions into reports
- Practical lab: executing a predictive analytics workflow
Best Practices for Enterprise-Level Analytics
- Governance frameworks and access control
- Managing large-scale datasets effectively
- Strategies for cost management
- Case studies highlighting successful implementations
Conclusion and Future Directions
Requirements
- Fundamental understanding of SQL
- Familiarity with data management principles
- Prior experience with analytics or reporting 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.
