Visão Geral

• Compreender os conceitos fundamentais do DuckDB.
• Instalar e configurar o DuckDB em diferentes ambientes.
• Executar consultas SQL eficientes utilizando o DuckDB.
• Integrar o DuckDB com linguagens de programação e pipelines de dados.
• Implementar técnicas de otimização de desempenho para grandes volumes de dados.

Objetivo

• Compreender os conceitos fundamentais do DuckDB.
• Instalar e configurar o DuckDB em diferentes ambientes.
• Executar consultas SQL eficientes utilizando o DuckDB.
• Integrar o DuckDB com linguagens de programação e pipelines de dados.
• Implementar técnicas de otimização de desempenho para grandes volumes de dados.

Publico Alvo

• Cientistas de dados que buscam uma solução de banco de dados leve para análise de dados.
• Engenheiros de dados que trabalham com processamento de grandes volumes de dados.
• Desenvolvedores e analistas que desejam integrar o DuckDB em suas aplicações e pipelines de dados.

Pre-Requisitos

• Conhecimento básico de SQL.
• Familiaridade com conceitos de banco de dados relacionais.
• Noções de linguagens de programação como Python ou R podem ser úteis.

Materiais

Conteúdo Programatico

Módulo 1: Introduction to DuckDB

1. What is DuckDB?
2. DuckDB’s architecture and in-memory processing.
3. Comparison with other analytical database systems.
4. Key features and use cases of DuckDB.

Módulo 2: Installing and Configuring DuckDB

1. System requirements for DuckDB.
2. Installing DuckDB on different platforms (Windows, macOS, Linux).
3. Configuring DuckDB for optimal performance.
4. Overview of DuckDB CLI and integration with other tools.

Módulo 3: SQL Basics in DuckDB

1. Basic SQL queries in DuckDB.
2. Differences between DuckDB SQL and traditional SQL.
3. Working with SELECT, WHERE, GROUP BY, and JOIN operations.
4. Using aggregate functions and window functions in DuckDB.

Módulo 4: Data Ingestion and Management

1. Importing and exporting data from CSV, Parquet, and other formats.
2. Handling large datasets with DuckDB.
3. Working with temporary and persistent tables.
4. Best practices for efficient data loading in DuckDB.

Módulo 5: Advanced Querying Techniques

1. Complex queries with subqueries and CTEs.
2. Using DuckDB's built-in functions and extensions.
3. Time series analysis and JSON data handling.
4. Query optimization and indexing strategies.

Módulo 6: Integrating DuckDB with Other Tools

1. Using DuckDB with Python (duckdb-python API).
2. Integration with R for data analysis.
3. Connecting DuckDB to Jupyter notebooks.
4. Using DuckDB with pandas for efficient data manipulation.

Módulo 7: Performance Optimization in DuckDB

1. Understanding DuckDB’s query execution engine.
2. Memory management and in-memory processing optimization.
3. Best practices for optimizing large-scale queries.
4. Profiling queries and diagnosing performance bottlenecks.

Módulo 8: DuckDB in Analytical Pipelines

1. DuckDB as part of a data pipeline.
2. Using DuckDB with cloud storage (S3, Azure Blob, etc.).
3. Integrating DuckDB with data engineering workflows.
4. Case study: Building a data pipeline with DuckDB.

Módulo 9: Security and Data Governance in DuckDB

1. Data security best practices in DuckDB.
2. Handling sensitive data and encryption.
3. Access control and authentication.
4. Implementing data governance policies with DuckDB.

Módulo 10: Case Studies and Best Practices

1. Real-world use cases of DuckDB.
2. Implementing DuckDB in production environments.
3. Best practices for long-term maintenance and scalability.
4. Future developments and the DuckDB roadmap.