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Course Overview
This three‐day accelerated course presents key performance capabilities, Snowflake‐recommended best practices, and tuning techniques to help participants use the Snowflake AI Data Cloud to develop diverse, high‐performance workloads. The course illustrates Snowflake‐recommended performance design best practices and how to identify pitfalls so participants can apply the methodology and features to their varied workloads.
The course is delivered on the latest version of the Snowflake AI Data Cloud, including new performance features and emerging features in public preview.
This course consists of lectures with many practical examples in instructor‐led demos, followed by experiential exercises.
Who Should Attend
- Data Application Developers
- Data Architects
- Data Engineers
- Snowflake Administrators
- Technical Team Leads
Course Objectives
- Work effectively and efficiently with the key capabilities and best practices behind Snowflake’s AI Data Cloud architecture which is designed for performance and scale.
- Apply the appropriate Snowflake‐provided tools for performance assessment and optimization.
- Use Snowflake’s tuning methodology on established performance features, including recommended best practices and avoiding pitfalls to work efficiently with the platform.
- Explore emerging performance features with suitable use cases and adoption of recommended best practices.
- Formulate diverse, high‐performance, and efficient workloads, including data transformations, ana‐ lytic applications, and data sharing.
- Achieve cost optimization by working effectively and monitoring the Snowflake AI Data Cloud.
Course Outline
1 - Anatomy of a Query
- Query Optimizations
- Query Execution
2 - Constructing Performant Queries
- Filtering Data
- Joining Data
- Aggregating, Ordering, and Grouping Data
- Subqueries and CTEs
- Estimating and Sampling
3 - Virtual Warehouse Optimization
- Virtual Warehouse Types
- Virtual Warehouse Settings
- Monitor Virtual Warehouse Efficiency
4 - Automatic Clustering Service
- Overview
- Evaluate Ordering (Clustering)
- Implement and Test Cluster Keys
- Create a Cluster Key
- Monitor Clustering Cost
- Clustering: Common Misconceptions
5 - Materialized Views
- Why Materialized Views?
- Monitor Materialized Views
6 - Dynamic Tables
- Create Dynamic Tables
- Monitor Dynamic Tables
- Dynamic Tables vs. Materialized Views
7 - Search Optimization Service
- Overview
- How it Works
- Add a Search Optimization
- Monitor Search Optimization Cost
8 - Query Acceleration Service
- Overview
- Identify Eligible Queries
- Configure Query Acceleration
- Monitor Query Acceleration
9 - Other Performance Considerations
- Hybrid Tables Overview and Performance
- Accessing External Data
- Apache Iceberg™ Tables in Snowflake Overview and Performance
- Memoizable Functions Overview and Performance
10 - Looking For Trouble (Issues and Inefficiencies)
- Finding Trouble in the Query_History
- Finding Problematic Query Operators
- Finding Patterns and Trends in Queries
- Finding Inefficiencies