AWS Analytics on Snowflake
Contact us to book this course
On-Site, Virtual
1 day
This comprehensive, hands-on training demonstrates how to monitor, optimize, and secure Snowflake data platforms running on AWS infrastructure. Students will learn how to establish visibility into query performance, resource utilization, and operational costs from initial deployment through production scale. By the end of the day, participants will be able to implement monitoring frameworks that provide actionable insights into Snowflake workloads, optimize warehouse performance, and establish governance controls that align with enterprise operational standards.
Learning Objectives
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Articulate the Snowflake architecture and its implications for monitoring and observability strategies.
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Configure query monitoring to track performance metrics, identify bottlenecks, and optimize execution plans.
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Implement warehouse utilization tracking and auto-scaling policies to balance performance with cost efficiency.
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Monitor data pipeline health across ingestion, transformation, and consumption layers.
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Establish governance observability using audit logs, access tracking, and compliance reporting.
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Automate alerting workflows using Snowflake's native monitoring capabilities and AWS integration points.
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Create operational dashboards that provide real-time visibility into platform health and business metrics.
Who Should Attend
Data platform engineers, cloud architects, DataOps practitioners, and technical analysts responsible for operating Snowflake environments on AWS. Prior experience with AWS services (S3, IAM, CloudWatch) is required. Familiarity with SQL and basic data warehousing concepts is helpful but not required. Experience with monitoring and observability tools in AWS environments will be helpful for understanding integration patterns and alerting workflows.
Course outline
- The Snowflake Three-Layer Architecture
- Storage, Compute, and Cloud Services Layers
- Why Traditional Monitoring Approaches Don't Apply
- Virtual Warehouses as Independent Compute Units
- The Separation of Storage and Compute
- Demo: Exploring a Snowflake Virtual Warehouse
- Query History and Query Profile
- Reading Execution Plans and Identifying Bottlenecks
- Micro-partition Pruning and Clustering Impact
- Warehouse Queue Monitoring
- Query Compilation and Caching Behavior
- Demo: Analyzing Query Performance Using Query Profile
- Virtual Warehouse Sizing and Performance Characteristics
- Credit Consumption Patterns
- Auto-Suspend and Auto-Resume Configuration
- Multi-Cluster Warehouse Scaling Policies
- Warehouse Load and Queueing Metrics
- Demo: Monitoring Warehouse Utilization with WAREHOUSE_METERING_HISTORY
- Credit-Based Billing Model
- Storage Costs vs. Compute Costs
- Resource Monitors and Budget Alerts
- Compute Credit Attribution by Warehouse
- Storage Growth Tracking
- Demo: Building a Cost Dashboard Using ACCOUNT_USAGE Schema
- Fintech: High-Frequency Fraud Detection
- Healthcare: Redacting PII with Sensitive Data Scanner
- Media: Monitoring Global Streaming Performance
- Access History and Query Attribution
- Role-Based Access Control Auditing
- Data Classification and Tag-Based Policies
- Login History and Authentication Monitoring
- Object Access Patterns
- Lab: Implementing Audit Reporting with ACCESS_HISTORY
