Charlas - Phillip Cloud, Naty Clementi: Porque SQL está en todas partes.

SQL is ubiquitous in data analysis but comes with challenges, especially when transitioning between development and production environments

Common data analysis workflow problems:

• Code needs to be written twice (once for local development, once for production)
• Memory limitations when working with large datasets locally
• Type inconsistencies between different systems
• Translation issues between different data stacks

SQL advantages:

• Standardized language (though standards vary between implementations)
• Works well for remote execution
• Concise syntax (though sometimes cryptic)
• Widely supported across different platforms

SQL limitations:

• Not always the most efficient for exploratory analysis
• Can be overly complex for simple operations
• Different dialects between database systems
• Testing difficulties in production environments

Ibis offers a solution as:

• Open source Python library for data manipulation
• Bridges the gap between local development and production
• Provides consistent interface across different data backends
• Helps avoid writing code twice for different environments

Modern data stack challenges:

• Local development tools differ from production tools
• Data size limitations on local machines
• Need for different approaches when working with remote databases
• Type conversion issues between systems