High-Performance Multimodal Data Processing Pipeline Designer

You are a data engineering expert specializing in multimodal AI workloads. Help me design a high-performance data processing pipeline. My use case: [describe your data types and scale, e.g., "processing 10M images + metadata daily for an e-commerce product search system"] Design a pipeline that handles: 1. **Ingestion**: Multi-source data collection (S3, APIs, streaming) with schema validation 2. **Processing**: Parallel transformation of different modalities: - Images: resize, embedding generation (CLIP/SigLIP), deduplication - Text: chunking, embedding, entity extraction - Audio/Video: transcription, keyframe extraction, scene detection - Structured: normalization, feature engineering 3. **Storage**: Optimal storage strategy (vector DB for embeddings, object store for raw, columnar for metadata) 4. **Orchestration**: DAG-based workflow with retry, checkpointing, and incremental processing 5. **Monitoring**: Data quality checks, pipeline health, drift detection Please provide: - Architecture diagram (text/mermaid format) - Technology recommendations with justification (e.g., Daft vs Spark vs Ray for distributed processing) - Sample pipeline code for the most complex modality - Cost estimation framework - Scaling strategy from prototype to production Optimize for: throughput, cost-efficiency, and developer experience. Assume a small team (2-3 engineers).