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Duration 14 hours
Course Outline
Foundations of BigQuery
- Core architecture and key features of BigQuery
- Understanding the cost model and pricing mechanics
- Overview of query execution and storage mechanisms
Query Optimization and Cost Management
- Techniques for tuning query performance
- Utilizing partitioned and clustered tables
- Strategies for monitoring and analyzing query efficiency
- Practical lab: Refining queries for maximum cost-efficiency
Data Ingestion and Transformation Processes
- Integrating data from diverse external sources
- Leveraging Dataflow and Dataprep for ETL workflows
- Implementing materialized views and scheduled queries
- Practical lab: Constructing an end-to-end reporting pipeline
Getting Started with BigQuery ML
- Introduction to 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 foundational model
Developing Predictive Models with BigQuery ML
- Processes for training and evaluating model accuracy
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Incorporating predictive insights into business reports
- Practical lab: Executing a complete predictive analytics workflow
Enterprise Analytics Best Practices
- Establishing governance and access control frameworks
- Managing large-scale datasets effectively
- Implementing strategic cost control measures
- Reviewing case studies of successful enterprise deployments
Conclusion and Future Directions
Requirements
- Foundational proficiency in SQL
- Understanding of core data management principles
- Prior exposure to 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.
