Visão Geral

O Curso Snowflake Architecture foi criado para fornecer uma compreensão profunda sobre os componentes e princípios arquiteturais da plataforma Snowflake Data Cloud.
Durante o treinamento, os participantes aprenderão como o Snowflake separa computação, armazenamento e serviços, além de explorar como esses elementos trabalham em conjunto para oferecer escalabilidade elástica, alto desempenho, segurança corporativa e custos otimizados.

O curso combina fundamentos teóricos, demonstrações práticas e laboratórios, permitindo que o participante compreenda não apenas o funcionamento técnico da arquitetura, mas também como aplicá-la de forma eficiente em projetos reais de dados corporativos.

Objetivo

Após realizar o curso Snowflake Architecture, você será capaz de:

• Entender a estrutura e camadas que compõem o Snowflake Data Cloud
• Explicar a separação entre compute, storage e services layer
• Configurar e gerenciar warehouses, databases e schemas de forma eficiente
• Compreender como o Snowflake entrega elasticidade, segurança e performance
• Aplicar melhores práticas de arquitetura e governança em ambientes corporativos

Publico Alvo

• Engenheiros de Dados e Arquitetos de Dados
• Administradores de Banco de Dados (DBAs)
• Desenvolvedores e Analistas de BI
• Profissionais de Cloud e Engenharia de Software que desejam compreender a arquitetura do Snowflake
• Líderes técnicos e gestores que planejam migrações e implementações de Data Warehouse em nuvem

Pre-Requisitos

• Conhecimento básico de bancos de dados relacionais (SQL)
• Familiaridade com conceitos de Data Warehouse e ETL
• Noções gerais de arquitetura em nuvem (AWS, Azure ou GCP)

Materiais

Conteúdo Programatico

Module 1: Introduction to Snowflake Data Cloud

1. Overview of Snowflake and its role in modern data architecture
2. Snowflake vs. traditional data warehouses
3. Key architectural innovations
4. Understanding the Data Cloud ecosystem
5. Deployment models across AWS, Azure, and GCP

Module 2: Core Snowflake Architecture Overview

1. Snowflake’s three-layer architecture explained
2. Storage Layer
3. Compute Layer (Virtual Warehouses)
4. Services Layer (Metadata, Optimization, Security)
5. Separation of compute and storage: benefits and use cases
6. Elastic scalability and multi-cluster architecture

Module 3: Storage Layer Deep Dive

1. How Snowflake stores structured and semi-structured data
2. Micro-partitioning and data clustering concepts
3. Automatic compression and optimization
4. Time Travel and Fail-safe mechanisms
5. Data encryption and lifecycle management

Module 4: Compute Layer Deep Dive

1. Virtual warehouses: configuration and scaling
2. Concurrency management and multi-cluster warehouses
3. Resource monitors and auto-suspend/resume
4. Query execution model and caching layers
5. Best practices for compute performance

Module 5: Services Layer Deep Dive

1. Query processing and optimization engine
2. Metadata management and transaction consistency
3. Security and access control in the services layer
4. Role-based access control (RBAC) and policies
5. Data sharing and governance mechanisms

Module 6: Snowflake Data Sharing and Multi-Tenant Architecture

1. Secure Data Sharing architecture
2. Data Exchange and Marketplace
3. Cross-region and cross-cloud data replication
4. Reader accounts and external data consumers
5. Data governance in shared environments

Module 7: Networking, Security, and Compliance

1. Snowflake network architecture and connectivity options
2. PrivateLink, VPC/VNet peering, and secure endpoints
3. Encryption mechanisms (in transit and at rest)
4. Key management and access policies
5. Compliance standards: SOC 2, HIPAA, ISO, GDPR

Module 8: Performance and Scalability Architecture

1. Query optimization process and adaptive execution
2. Caching mechanisms: result, metadata, and data caches
3. Best practices for high-concurrency environments
4. Scaling compute for ETL, analytics, and ML workloads
5. Performance troubleshooting and cost control strategies

Module 9: Integrations and Ecosystem Architecture

1. Integration with BI tools (Power BI, Tableau, Looker)
2. Data ingestion via Snowpipe and Kafka connectors
3. Integration with Azure Data Factory, Airflow, and Informatica
4. Working with Snowpark and dbt (data build tool)
5. API and REST architecture overview

Module 10: Hands-on Labs

1. Exploring Snowflake layers using the UI and CLI
2. Setting up warehouses and performing scaling tests
3. Configuring secure data sharing between accounts
4. Analyzing query plans and caching behavior
5. Architecture visualization and mapping exercise

Module 11: Best Practices and Design Patterns

1. Reference architectures for analytics, ETL, and data science
2. Hybrid and multi-cloud deployment models
3. Governance, security, and compliance best practices
4. Cost optimization and resource planning
5. Architecture review checklist for production readiness

Module 12: Future and Advanced Architectural Topics

1. Snowflake Unistore architecture
2. Data Lake and Lakehouse integration
3. Native Apps and Snowflake Marketplace architecture
4. Dynamic Tables and Data Engineering pipelines
5. Future directions in Snowflake architecture evolution