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Duration 14 hours
Course Outline
BigQuery Fundamentals
- Architecture and key features of BigQuery
- Explanation of cost models and pricing frameworks
- General overview of query execution and storage mechanisms
Query Optimization and Cost Reduction
- Techniques for tuning queries
- Implementation of partitioned and clustered tables
- Monitoring and assessing query performance
- Practical lab: optimizing queries for cost efficiency
Data Ingestion and Transformation Processes
- Importing data from external sources
- Leveraging Dataflow and Dataprep for ETL workflows
- Utilizing materialized views and scheduled queries
- Practical lab: constructing a reporting pipeline
Getting Started with BigQuery ML
- Introduction to machine learning capabilities within BigQuery
- Supported model types (including linear regression, logistic regression, clustering, etc.)
- SQL syntax for defining ML models
- Practical lab: creating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating model performance
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Incorporating predictions into business reports
- Practical lab: executing a predictive analytics workflow
Enterprise Analytics Best Practices
- Governance strategies and access control mechanisms
- Managing large-scale datasets efficiently
- Strategies for controlling costs
- Analysis of successful implementation case studies
Recap and Future Steps
Requirements
- Foundational understanding of SQL
- Familiarity with core data management principles
- Prior experience using reporting or analytics platforms
Intended 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.
