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Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost model and pricing structure
- Overview of query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for query tuning
- Implementation of partitioned and clustered tables
- Monitoring and analyzing query performance
- Hands-on lab: Optimizing queries for cost efficiency
Data Ingestion and Transformation
- Loading data from external sources
- Utilizing Dataflow and Dataprep for ETL processes
- Leveraging materialized views and scheduled queries
- Hands-on lab: Constructing a reporting pipeline
Introduction to BigQuery ML
- Overview of machine learning capabilities in BigQuery
- Supported model types (e.g., linear regression, logistic regression, clustering)
- SQL syntax for ML model development
- Hands-on lab: Creating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating models
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Integrating predictions into reporting frameworks
- Hands-on lab: Executing a predictive analytics workflow
Best Practices for Enterprise Analytics
- Governance and access control strategies
- Managing large datasets at scale
- Cost control methodologies
- Case studies of successful implementations
Summary and Future Steps
Requirements
- Fundamental understanding of SQL
- Familiarity with core data management concepts
- Experience utilizing 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.
