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Duration 14 hours
Course Outline
Getting Started with BigQuery
- Core architecture and key features
- Cost structure and pricing models
- Overview of query processing and storage mechanisms
Enhancing Query Efficiency and Managing Expenses
- Techniques for optimizing query performance
- Utilizing partitioned and clustered tables
- Monitoring and analyzing query execution metrics
- Hands-on lab: Optimizing queries to minimize costs
Data Ingestion and Processing Workflows
- Importing data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- Implementing materialized views and scheduled queries
- Hands-on lab: Constructing a reporting pipeline
Introduction to BigQuery ML
- Overview of machine learning capabilities within BigQuery
- Supported model types (linear regression, logistic regression, clustering, etc.)
- SQL syntax for creating ML models
- Hands-on lab: Creating and training a basic model
Developing Predictive Models with BigQuery ML
- Processes for training and evaluating models
- Using ML.EVALUATE and ML.PREDICT functions
- Integrating model predictions into reports
- Hands-on lab: Executing a predictive analytics workflow
Enterprise Analytics Best Practices
- Governance and access control strategies
- Managing large-scale datasets efficiently
- Strategies for maintaining cost control
- Case studies of successful implementations
Conclusion and Future Steps
Requirements
- Fundamental understanding of SQL
- Familiarity with data management principles
- Prior experience with reporting or analytics tools
Target 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.
