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Course Outline
Introduction to BigQuery
- BigQuery architecture and 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.
- Hands-on lab: optimizing queries for cost efficiency.
Data Ingestion and Transformation
- Loading data from external sources.
- Employing Dataflow and Dataprep for ETL processes.
- Implementing materialized views and scheduled queries.
- Hands-on 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, etc.).
- SQL syntax for creating ML models.
- Hands-on lab: creating and training a model.
Building Predictive Models with BigQuery ML
- Training and evaluating models.
- Using ML.EVALUATE and ML.PREDICT functionalities.
- Integrating predictions into reports.
- Hands-on lab: predictive analytics workflow.
Best Practices for Enterprise Analytics
- Governance and access control.
- Managing large datasets at scale.
- Strategies for cost control.
- Case studies of successful implementations.
Summary and Next Steps
Requirements
- Fundamental knowledge of SQL.
- Familiarity with data management concepts.
- Experience with reporting or analytics tools.
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.
