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Course Outline
Foundations of BigQuery
- BigQuery architecture and key features
- Cost models and pricing details
- Insights into query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for tuning queries
- Utilization of partitioned and clustered tables
- Tracking and evaluating query performance
- Practical lab: optimizing queries for cost efficiency
Data Ingestion and Transformation Workflows
- Importing data from external sources
- Employing Dataflow and Dataprep for ETL processes
- Implementing materialized views and scheduled queries
- Practical lab: constructing a reporting pipeline
Getting Started with BigQuery ML
- Understanding machine learning capabilities in BigQuery
- Supported model types (including linear regression, logistic regression, clustering, etc.)
- SQL syntax for defining ML models
- Practical lab: generating and training a model
Developing Predictive Models with BigQuery ML
- Training and assessing models
- Applying ML.EVALUATE and ML.PREDICT functions
- Incorporating predictions into reports
- Practical lab: executing a predictive analytics workflow
Best Practices for Enterprise-Scale Analytics
- Governance and access control strategies
- Managing extensive datasets at scale
- Strategies for cost management
- Analysis of successful implementation case studies
Conclusion and Path Forward
Requirements
- Foundational SQL proficiency
- Familiarity with data management principles
- Prior 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.
