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Snowflake Architect

Mastering Architecture, Performance Optimization, and Best Practices in Snowflake

Duration

3 Days

Level

Intermediate Level

Design and Tailor this course

Official Course

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This three-day course for Architects and Technical Leaders provides the skills, knowledge, and Snowflake best practices to deploy and operate Snowflake, insights and recommendations based upon real-world customer experiences, and the confidence to get the very best out of Snowflake’s technology.

ACQUIRED SKILLS

  • Examine the tradeoffs associated with the available environment and Snowflake Account deployment
  • options.
  • Use the Snowflake data security framework to balance the often-conflicting needs of protecting sensitive
  • data while democratizing access and facilitating sharing.
  • Apply Snowflake best practices to maximize performance and efficient use of resources during data inges-
  • tion, transformation, and end user queries.
  • Analyze Snowflake metadata to identify performance and cost issues and recommend remedial action.
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  • Solution Architects
  • Data Architects
  • Database Architects
  • Enterprise Data Architects
  • Senior Data Engineers
  • Technical Team Leads
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  • Snowflake Architecture
  • Snowflake’s Layered Architecture
  • Organization
  • Geographic Account Considerations
  • Snowflake Security Domains
  • Environment Deployment Options
  • Cross Environment Data Transfer
  • Options
  • Environment Separation
  • Logical Data Architecture (Layers)
  • Physical Architecture Options
  • Database Considerations
  • Database Reference Options
  • Summary and Recommendations
  • Overall Data Flow
  • Reference Data Architecture
  • Handling Raw History
  • Integration with Data Lake
  • Create External Table
  • Query External Table
  • Partitioned External Tables
  • Snowpipe Streaming Overview
  • External Network Access
  • Native Apps
  • Change Data Capture, Creating and Managing Streams
  • Dynamic Tables
  • Hybrid Tables
  • Iceberg Tables in Snowflake
  • Objectives
  • Workload Challenges
  • Scale Up for Large Workloads
  • Key Concepts: Scaling Up
  • Key Concepts: Diminishing Elapsed Time Improvements
  • Scale Out for Multiple Concurrent Users
  • Speed vs. Throughput
  • Right-Sizing Virtual Warehouses
  • Virtual Warehouse Deployment Approach
  • Measuring Workloads
  • Summary
  • Case Study
  • Data Security Framework
  • Data Classification
  • Identify Data Sensitivity
  • Overview
  • RBAC Requirements
  • RBAC Hierarchy Design
  • RBAC Role Design
  • Naming Standards
  • RBAC Script Building
  • Snowflake RBAC Best Practices
  • Data Masking and Row Access Policies
  • Dynamic Data Masking
  • Row Access Policies
  • Summary
  • Use Case
  • Direct Share
  • With Replication
  • Data Mesh
  • Relevant Snowflake Capabilities for a Data Mesh
  • Data Mesh Architecture Options with Snowflake
  • Data Products in Snowflake
  • Auto-fulfillment
  • How Auto-fulfillment Works
  • Data Storage Methods
  • Snowflake Data Storage
  • Data Storage Implications
  • Time Travel
  • Key Point
  • Time Travel and Storage
  • Clones
  • Data Recovery
  • Agile Data Management
  • Development
  • System Testing
  • Deployment
  • What Is Table Clustering?
  • Partition Pruning (Elimination)
  • Overlapping Values
  • Evaluating Clustering
  • Implement and Test Clustering Keys
  • Search Optimization
  • How Does It Work?
  • Materialized Views
  • Materialized View Use Cases
  • Query Acceleration Service (QAS)
  • Observability on Snowflake
  • Outbound Notifications
  • Snowflake Alerts
  • Observability Within Snowsight
  • Budgets
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  • SQL skills, a background in database management, and, ideally, experience in designing and deploying
  • analytic-based systems are required.
  • Completion of “Snowflake Fundamentals” or equivalent Snowflake knowledge.

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