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Duration 14 hours
Course Outline
Introduction to BigQuery
- BigQuery architecture and key features
- Cost models and pricing structures
- Overview of query execution and storage mechanisms
Query Optimisation and Cost Reduction
- Query tuning methodologies
- Partitioned and clustered tables
- Monitoring and analysing query performance
- Hands-on lab: Optimising queries for cost efficiency
Data Ingestion and Transformation
- Loading data from external sources
- Leveraging Dataflow and Dataprep for ETL processes
- 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 defining ML models
- Hands-on lab: Creating and training a model
Developing Predictive Models with BigQuery ML
- Training and evaluating models
- Utilising ML.EVALUATE and ML.PREDICT
- Integrating predictions into reports
- Hands-on lab: Executing a predictive analytics workflow
Best Practices for Enterprise Analytics
- Governance and access control
- Managing large datasets at scale
- Case studies of successful implementations
Summary and Next Steps
Requirements
- Foundational knowledge 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.
