The pragmatic Pythonic data engineer [PyCon DE & PyData Berlin 2024]

Data engineering requires a pragmatic approach focused on solving real problems rather than chasing trendy tools or silver bullets

Key components of a data product architecture:

• Data sources and ingestion
• Common data format for transfer/storage (like Parquet/Arrow)
• Data validation and testing
• Orchestration and monitoring
• Serving layer for consumption

Data validation is critical:

• Use tools like Great Expectations for data quality checks
• Pydantic for business validation rules
• Test data as rigorously as code
• Implement validation in production pipelines

Apache Airflow best practices:

• Organize DAGs in standard folder structure
• Use templating for reusability
• Implement proper testing of DAGs and operators
• Leverage built-in monitoring and alerting
• Create custom operators when needed

PySpark considerations:

• Handles Python-Java serialization automatically
• Good for large-scale data processing
• Works well with pandas DataFrames
• Supports both batch and streaming
• Native Python integration without heavy Java configuration

Data scale and architecture choices:

• Consider data volume when choosing tools
• Use partitioning for large datasets
• Balance cost vs performance
• Traditional methods still valid for smaller scales
• Think about future scaling needs

Observability and monitoring:

• Track pipeline health
• Monitor data quality
• Set up alerts for validation errors
• Use metrics for optimization
• Integrate with existing monitoring tools