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Course Outline
Overview of BigQuery
- BigQuery architecture and key features
- Pricing models and cost structures
- Fundamentals of query execution and storage
Query Optimization and Cost Reduction
- Techniques for tuning queries
- Utilizing partitioned and clustered tables
- Tracking and analyzing query performance
- Practical lab: refining queries for cost-effectiveness
Data Ingestion and Transformation
- Importing data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- Implementing materialized views and scheduled queries
- Practical lab: constructing a reporting pipeline
Introduction to BigQuery ML
- Insights into machine learning within BigQuery
- Supported model types (including linear regression, logistic regression, and clustering)
- SQL syntax for developing ML models
- Practical lab: creating and training a model
Developing Predictive Models with BigQuery ML
- Model training and evaluation techniques
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Embedding predictions into reporting outputs
- Practical lab: implementing a predictive analytics workflow
Best Practices for Enterprise-Grade Analytics
- Governance frameworks and access control
- Managing extensive datasets at scale
- Strategies for cost management
- Case studies of successful enterprise implementations
Recap and Future Directions
Requirements
- Fundamental proficiency in SQL
- Understanding of core data management principles
- Prior exposure to 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.
