Visão Geral

O Curso Snowflake Fundamentals foi desenvolvido para profissionais que desejam adquirir uma base sólida na utilização da plataforma Snowflake Data Cloud, desde a criação de ambientes até a execução de consultas e integração com ferramentas externas.
Durante o treinamento, o participante aprenderá os conceitos essenciais de arquitetura, armazenamento, processamento, segurança, e otimização de consultas, com uma abordagem teórica e prática.

Por meio de laboratórios reais, os alunos irão configurar instâncias Snowflake, manipular dados e compreender o funcionamento dos principais componentes da plataforma, preparando-se para o uso corporativo e para certificações oficiais Snowflake.

Objetivo

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

• Compreender a arquitetura e os principais componentes do Snowflake
• Configurar e gerenciar ambientes Snowflake
• Carregar, consultar e transformar dados com eficiência
• Aplicar práticas de segurança e controle de acesso
• Entender conceitos fundamentais de performance, custos e governança

Publico Alvo

• Engenheiros e Analistas de Dados iniciantes em Snowflake
• Desenvolvedores, DBAs e Profissionais de BI que atuam com Data Warehousing
• Arquitetos de Dados e Engenheiros de Cloud que desejam compreender o ecossistema do Snowflake
• Profissionais interessados em migrar soluções de dados para a nuvem

Pre-Requisitos

• Noções básicas de bancos de dados relacionais (SQL)
• Familiaridade com conceitos de Data Warehouse e ETL
• Conhecimentos gerais sobre nuvem (AWS, Azure ou GCP)

Materiais

Conteúdo Programatico

Module 1: Introduction to Snowflake and the Data Cloud

1. What is Snowflake? Overview and key features
2. Evolution of cloud data platforms
3. Snowflake Data Cloud ecosystem
4. Snowflake Editions and deployment options (AWS, Azure, GCP)
5. Snowflake UI (Snowsight) and SnowSQL CLI overview

Module 2: Understanding Snowflake Architecture

1. The three-layer architecture: Storage, Compute, and Services
2. Separation of compute and storage explained
3. Virtual Warehouses and query execution process
4. Automatic scaling and caching concepts
5. Data storage format and micro-partitions

Module 3: Setting Up and Managing Snowflake Environments

1. Creating databases, schemas, and tables
2. Understanding roles, users, and privileges
3. Resource monitors and account usage views
4. Setting up warehouses and auto-suspend/resume features
5. Using worksheets and executing SQL commands

Module 4: Loading and Unloading Data

1. Internal and external stages overview
2. File formats supported by Snowflake (CSV, JSON, Parquet, Avro)
3. COPY INTO command for data loading
4. Data unloading and exporting best practices
5. Using Snowpipe for continuous data ingestion

Module 5: Working with Data in Snowflake

1. Querying data using SQL
2. Working with semi-structured data (VARIANT, JSON, and FLATTEN)
3. Views, temporary tables, and materialized views
4. Joins, aggregations, and analytical functions
5. Time Travel and data versioning

Module 6: Security, Access Control, and Governance

1. Role-Based Access Control (RBAC) in Snowflake
2. Managing users, roles, and privileges
3. Network policies and encryption
4. Data masking and secure data sharing
5. Monitoring account usage and resource consumption

Module 7: Query Optimization and Performance Basics

1. Query profile and execution plan overview
2. Warehouse sizing and scaling for performance
3. Understanding caching layers (result, data, metadata)
4. Avoiding anti-patterns in queries
5. Best practices for efficient query design

Module 8: Cost Management and Monitoring

1. Understanding Snowflake’s credit system
2. Warehouse usage and cost breakdown
3. Using Resource Monitors for cost control
4. Query cost optimization strategies
5. Practical exercises on cost management

Module 9: Integrations and Ecosystem

1. Connecting Snowflake with Power BI, Tableau, and Looker
2. Integration with Azure Data Factory and AWS Glue
3. Snowflake connectors for Python, Spark, and Kafka
4. Working with APIs and Snowflake Partner Connect
5. Data sharing and external table access

Module 10: Hands-on Labs

1. Create and manage your Snowflake environment
2. Load and query structured and semi-structured data
3. Implement secure data sharing between accounts
4. Analyze query performance and optimize results
5. Final project: build a mini data pipeline on Snowflake

Module 11: Best Practices and Certification Preparation

1. Snowflake best practices for data engineering
2. Security and compliance recommendations
3. Performance and cost optimization checklist
4. Introduction to the SnowPro Core Certification exam topics
5. Tips and resources for further learning

Module 12: Real-World Scenarios

1. Common architecture patterns in Snowflake projects
2. Migration from on-premise to Snowflake
3. Managing multi-cloud deployments
4. Data governance and collaboration strategies
5. Case study: designing a data platform using Snowflake