Architecting the Snowflake Data Cloud - Multi-cluster Shared Data Architecture

Overview

Delve into Snowflake's multi-cluster shared data architecture, exploring its evolution from a cloud data warehouse to a global, multi-cloud platform supporting diverse data processing workloads and secure collaboration.

Syllabus

• Introduction to Snowflake Data Cloud
Evolution of Snowflake: From Cloud Data Warehouse to Multi-cloud Platform
Overview of Multi-cluster Shared Data Architecture
• Core Concepts of Snowflake Architecture
Separation of Storage and Compute
Multi-cluster Warehouses
Virtual Warehouses: Scaling and Performance
• Storage Layer in Snowflake
Data Partitioning and Clustering
Time Travel and Fail-safe Mechanisms
Data Sharing and Global Data Replication
• Compute Layer in Snowflake
Dynamic Clustering for Workload Management
Auto-scaling and Load Balancing
Performance Optimization Techniques
• Security and Compliance
Access Control and Authentication
Data Encryption and Masking
Auditing and Monitoring
• Data Processing on Snowflake
ETL and ELT Workflows
Using Snowflake Streams and Tasks
Native and Third-party Tool Integrations
• Multi-cloud and Hybrid Cloud Implementations
Cross-Cloud Data Movement and Connectivity
Integration with Global Data Sharing
Strategies for Disaster Recovery
• Use Cases and Applications
Real-time Analytics and BI Tools
Machine Learning Workflows
Industry-specific Solutions
• Best Practices and Architectural Patterns
Designing for Scalability and Performance
Cost Management Strategies
Advanced Data Governance Techniques
• Future Trends and Innovations
Emerging Features and Capabilities
Roadmap for Enhancements in Snowflake
Potential Impacts of Global Data Policies
• Course Review and Capstone Project
Summary of Key Learnings
Project: Designing a Snowflake-Based Solution for a Real-world Scenario

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Subjects

Business