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Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost model and pricing structure
- Overview of query execution and storage mechanisms
Optimizing Queries and Reducing Costs
- Techniques for query tuning
- Utilizing partitioned and clustered tables
- Monitoring and analyzing query performance
- Practical lab: Optimizing queries for cost efficiency
Data Ingestion and Transformation
- Loading data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- Implementing materialized views and scheduled queries
- Practical lab: Building a reporting pipeline
Introduction to BigQuery ML
- Overview of machine learning capabilities in BigQuery
- Supported model types (including linear regression, logistic regression, clustering, and more)
- SQL syntax for managing ML models
- Practical lab: Creating and training a model
Building Predictive Models with BigQuery ML
- Training and evaluating models
- Utilizing ML.EVALUATE and ML.PREDICT functions
- Integrating predictions into reporting outputs
- Practical lab: Executing a predictive analytics workflow
Best Practices for Enterprise Analytics
- Governance and access control management
- Managing large datasets at scale
- Strategies for cost control
- Case studies showcasing successful implementations
Summary and Next Steps
Requirements
- Fundamental knowledge of SQL
- Familiarity with core data management concepts
- Prior experience with reporting or analytics tools
Target Audience
- Data analysts
- Business Intelligence (BI) developers
- Data engineers
14 Hours
Testimonials (3)
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.
Mircea
Course - Google Cloud Platform Basics and Management
Responses with solutions and practical use.
