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Duration 14 hours
Course Outline
Getting Started with BigQuery
- BigQuery architecture and key features
- Cost models and pricing frameworks
- Fundamentals of query execution and storage
Query Optimization and Cost Reduction
- Techniques for query tuning
- Implementation of partitioned and clustered tables
- Tracking and analyzing query performance metrics
- Practical lab: Enhancing queries for cost-effectiveness
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 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: Creating and training a model
Developing Predictive Models with BigQuery ML
- Training and assessing model performance
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Embedding predictions into business reports
- Practical lab: Executing a predictive analytics workflow
Enterprise Analytics Best Practices
- Data governance and access control mechanisms
- Managing extensive datasets at scale
- Strategies for cost management
- Case studies highlighting successful deployments
Wrap-up and Future Directions
Requirements
- Fundamental understanding of SQL
- Awareness of data management principles
- Prior experience 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.
