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Duration 14 hours
Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost structures and pricing models
- Overview of query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for tuning queries
- Utilizing partitioned and clustered tables
- Monitoring and analyzing query performance
- Practical lab: Optimizing queries for cost efficiency
Data Ingestion and Transformation
- Importing data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- 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, clustering, etc.)
- SQL syntax for implementing ML models
- Practical lab: Creating and training a model
Developing Predictive Models with BigQuery ML
- Training and assessing models
- Utilizing ML.EVALUATE and ML.PREDICT
- Integrating predictions into reports
- Practical lab: Executing a predictive analytics workflow
Best Practices for Enterprise Analytics
- Governance and access control strategies
- Managing large-scale datasets
- Cost management tactics
- Case studies of successful implementations
Conclusion and Path Forward
Requirements
- Foundational proficiency in SQL
- Understanding 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.
