Building a composable data pipeline using source-available components enables organizations to create flexible, scalable infrastructure that can evolve with business needs.

Foundation with Snowplow:

• Begin by leveraging Snowplow as the foundational data collector for comprehensive event tracking
• Snowplow's event tracking ensures reliable data collection across various touchpoints including web, mobile, and IoT devices
• Provides high-quality, schema-validated behavioral data as the foundation for your entire pipeline

Processing and transformation layer:

• Integrate Apache Kafka for high-performance event streaming and real-time data processing
• Use dbt for SQL-based transformations and analytics modeling within your data warehouse
• Implement Apache Flink or Apache Spark for real-time data processing and complex analytics workloads

Storage and enrichment:

• Use data lakes like Amazon S3 or Azure Data Lake for scalable, cost-effective storage
• Implement data enrichment using commercial tools like AWS Glue or dbt Cloud for enhanced analytics capabilities
• Ensure proper data lifecycle management and archiving strategies

Composability advantages:

• The key to composability lies in modularity, allowing you to swap and upgrade components independently
• Maintain standardized interfaces and data formats for seamless integration
• Enable gradual migration and technology adoption without disrupting existing workflows