Rodel van Rooijen - Building a Data Platform from scratch | PyData Amsterdam 2024

When building a data platform from scratch, prioritize using open source tools where possible to control costs while maintaining flexibility

Choose cloud platforms and tools based on existing team expertise and experience rather than reinventing the wheel

Key components needed:

• Storage and querying layer (e.g. BigQuery)
• Batch/streaming transformation layer
• Orchestration layer (e.g. Airflow)
• Visualization/BI layer
• Import/export capabilities
• Change data capture layer

Consider three main deployment options:

• Self-hosted open source (most control, higher maintenance)
• Managed open source (balanced approach)
• Proprietary solutions (fastest implementation but most expensive)

Start with batch processing before introducing streaming to reduce initial complexity

Design for horizontal scaling from the beginning using managed Kubernetes platforms

Factor in total cost of ownership including:

• License fees
• Infrastructure costs
• Team expertise requirements
• Maintenance overhead

Build continuous integration/deployment capabilities early to handle platform changes effectively

Consider embedding analytics into existing products as a value-add service

Evaluate which AI capabilities align with business needs before implementation

Think about monetization strategy and business value early in the platform development process