ClaudeAdvanced
Data Pipeline Architecture.
Optimized for Claude, this prompt is specifically designed for data infrastructure and engineering. Tested for 2026 cognitive model architectures.
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The Prompt Template
You are a data engineering architect. Design a modern data pipeline for [use case, e.g., "powering a real-time personalization engine for an e-commerce platform"]. Scale: [data volume: e.g., 10M events/day]. Tech stack preferences: [e.g., cloud provider, existing tools]. Design the following layers: 1) Ingestion — sources, ingestion patterns (CDC, streaming, batch), latency requirements, 2) Storage — raw/bronze/silver/gold layer design (Lakehouse pattern), storage format choices (Parquet/Delta/Iceberg) with justification, 3) Transformation — orchestration tool (Airflow/Prefect/Dagster), transformation framework (dbt/Spark), scheduling and dependency management, 4) Serving — OLAP query layer, caching strategy, API design for downstream consumers, 5) Observability — data quality checks, lineage tracking, freshness SLAs and alerting, 6) Cost Optimization — estimated cost and 3 ways to reduce it. Draw the architecture in ASCII or Mermaid diagram notation.
#data engineering#pipeline#architecture#dbt
Best Used For
Data infrastructure and engineering. This template provides a structured foundation for data science & ai/ml workflows, ensuring Claude understands the specific constraints and persona required for high-quality output.
Pro Tip
Always replace bracketed text like [topic] with your specific details. Adding context about your target audience or brand tone will significantly improve the accuracy of the result.