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Course Outline
Getting Started with BigQuery
- Understanding BigQuery's architecture and core capabilities
- Exploring the cost model and pricing framework
- Overview of query execution mechanisms and storage structures
Enhancing Query Efficiency and Managing Costs
- Techniques for fine-tuning queries
- Implementing partitioned and clustered tables
- Monitoring and analyzing query performance metrics
- Hands-on lab: applying optimization strategies for cost savings
Data Ingestion and Transformation Workflows
- Loading data from various external sources
- Utilizing Dataflow and Dataprep for ETL processes
- Leveraging materialized views and scheduled queries
- Hands-on lab: constructing a complete 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 creating ML models
- Hands-on lab: building and training a custom model
Developing Predictive Models with BigQuery ML
- Process for training and evaluating models
- Using ML.EVALUATE and ML.PREDICT functions
- Integrating model predictions into reporting tools
- Hands-on lab: executing a full predictive analytics workflow
Enterprise Analytics Best Practices
- Governance strategies and access control mechanisms
- Managing large-scale datasets effectively
- Strategies for controlling costs
- Case studies highlighting successful implementations
Recap and Future Steps
Requirements
- Fundamental proficiency in SQL
- Understanding of data management principles
- Practical experience with reporting or analytics platforms
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.
