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 Duration 14 hours

Course Outline

Overview of BigQuery Capabilities

  • Structural components and functional attributes
  • Billing frameworks and rate structures
  • Fundamental processes for query processing and data retention

Query Efficiency and Financial Management

  • Methods for refining query performance
  • Implementation of partitioned and clustered storage structures
  • Continuous monitoring and analysis of operational metrics
  • Practical exercise: Enhancing cost-effectiveness in query execution

Data Acquisition and Processing

  • Ingesting data from various external systems
  • Leveraging Dataflow and Dataprep for extraction, transformation, and loading
  • Utilization of materialized views and automated query scheduling
  • Practical exercise: Developing a streamlined reporting architecture

Introduction to BigQuery Machine Learning

  • Application of machine learning within the BigQuery environment
  • Supported algorithm types, including linear and logistic regression and clustering
  • SQL protocols for model development
  • Practical exercise: Construction and training of analytical models

Development of Predictive Models using BigQuery ML

  • Procedures for model training and validation
  • Application of ML.EVALUATE and ML.PREDICT functions
  • Incorporation of forecasts into official reporting documents
  • Practical exercise: End-to-end predictive analytics process

Enterprise Analytics Standards and Protocols

  • Data governance and authorization controls
  • Administration of large-scale data assets
  • Strategies for maintaining fiscal responsibility
  • Review of proven deployment scenarios

Conclusion and Future Recommendations

Requirements

  • Foundational proficiency in SQL
  • Understanding of core data management principles
  • Prior exposure to reporting or analytical platforms

Target Audience

  • Data analysts
  • Business intelligence developers
  • Data engineers

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