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Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost structure and pricing models
- Fundamentals of query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for tuning query performance
- Implementation of partitioned and clustered tables
- Monitoring and analyzing query execution efficiency
- Hands-on lab: Optimizing queries for maximum cost-efficiency
Data Ingestion and Transformation Strategies
- Importing data from diverse external sources
- Leveraging Dataflow and Dataprep for ETL processes
- Utilizing materialized views and scheduled queries
- Hands-on lab: Constructing a robust reporting pipeline
Overview of 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
- Hands-on lab: Creating and training a basic model
Developing Predictive Models with BigQuery ML
- Processes for training and evaluating model accuracy
- Application of ML.EVALUATE and ML.PREDICT functions
- Integrating model predictions into business reports
- Hands-on lab: End-to-end predictive analytics workflow
Best Practices for Enterprise-Grade Analytics
- Governance frameworks and access control protocols
- Strategies for managing large-scale datasets
- Advanced cost control and optimization strategies
- Analysis of successful real-world implementation case studies
Conclusion and Future Directions
Requirements
- Foundational knowledge of SQL
- Understanding of core data management concepts
- Practical experience with reporting or analytics tools
Target Audience
- Data analysts
- BI developers
- Data engineers
14 Hours
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.
