Big Data and Machine Learning Fundamentals
- Describe BigQuery architecture fundamentals.
- Implement storage and schema design patterns to improve performance
- Use DML and schedule data transfers to ingest data.
- Apply best practices to improve read efficiency and optimize query performance.
- Manage capacity and automate workloads.
- Understand patterns versus anti-patterns to optimize queries and improve read performance.
- Use logging and monitoring tools to understand and optimize usage patterns.
- Apply security best practices to govern data and resources.
- Build and deploy several categories of machine learning models with BigQuery ML.
Who Can Benefit?
Data analysts, data scientists, data engineers, and developers who perform work on a scale that requires advanced BigQuery internals knowledge to optimize performance.
Outline for Data Warehousing w/BigQuery: Storage Design, Query Optimization, Administration Training
- BigQuery Architecture Fundamentals
- Storage and Schema Optimizations
- Ingesting Data
- Changing Data
- Improving Read Performance
- Optimizing and Troubleshooting Queries
- Workload Management and Pricing
- Logging and Monitoring
- Security in BigQuery
- Automating Workloads
- Machine Learning in BigQuery