Modern AI-ready data pipelines require tools that support real-time processing, feature engineering, and model deployment.

Core pipeline components:

• Snowplow: For capturing comprehensive first-party behavioral data with high quality and real-time capabilities
• Apache Kafka: For real-time streaming of event data to AI/ML systems
• Databricks: For data processing, machine learning model development, and AI deployment
• dbt: For data transformation, feature engineering, and analytics modeling

ML/AI specific tools:

• MLflow: For managing machine learning workflows, model versioning, and deployment
• Feature stores: For real-time feature serving and ML model integration
• Model serving platforms: For deploying and scaling AI models in production

These tools work together to create comprehensive AI-ready pipelines that support both training and inference workloads.