The Struggles We Skipped: Data Engineering for the TikTok Generation [PyCon DE & PyData Berlin 2024]

DLT (Data Load Tool) is an open-source Python library that simplifies ETL/ELT processes by handling data pipeline creation and unstructured data normalization

The tool automatically handles nested data structures by creating parent-child relationships between tables and normalizing data without manual coding

Key features include:

• Automatic schema detection and data unnesting
• Support for incremental loading and merge operations
• Integration with common tools like Airflow and DBT
• Async function support for parallel processing
• Works with multiple data sources and destinations

Data engineering challenges in modern development:

• Dealing with unstructured data from various sources
• Managing multiple API endpoints and authentication
• Constant changes in tools and frameworks
• Limited time for proper ETL development
• Cost considerations for different solutions

Benefits for junior developers and analysts:

• No steep learning curve
• Natural Python integration
• Reduces boilerplate code
• Allows focus on analysis rather than pipeline building
• Open source community support

Implementation involves simple steps:

• Pipeline declaration with destination
• Resource definition
• Source configuration
• Pipeline execution

Cost-effective solution that supports:

• Multiple data sources
• Schema control
• YAML configuration
• Reusable components
• Various transformation options