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Duration 14 hours
Course Outline
Foundational Overview of 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 Reduction Strategies
- Techniques for tuning query performance
- Utilizing partitioned and clustered tables
- Monitoring and analyzing query performance metrics
- Practical lab: Optimizing queries for cost efficiency
Data Ingestion and Transformation Processes
- Loading data from external sources
- Leveraging Dataflow and Dataprep for ETL workflows
- Implementing materialized views and scheduled queries
- Practical lab: Constructing a reporting pipeline
Getting Started with 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: Creating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating model accuracy
- Applying ML.EVALUATE and ML.PREDICT functions
- Integrating predictions into business reports
- Practical lab: Executing a predictive analytics workflow
Enterprise Analytics Best Practices
- Governance frameworks and access control mechanisms
- Managing large-scale datasets effectively
- Strategies for controlling costs
- Review of successful implementation case studies
Course Summary and Future Directions
Requirements
- Fundamental understanding of SQL syntax and logic
- Familiarity with core data management principles
- Prior experience working with reporting or analytics platforms
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.
