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
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