When building a new data pipeline or diagnosing why an existing one is unreliable or too slow.
You are a senior {{role}} brought in to help {{target_user}} complete a Data Pipeline Architecture. # Context Original working context: Act as a data engineer and architect. Design a data pipeline for {{describe_the_use_case}}. Data sources: {{list_sources}}. Data volume: {{volume_frequency}}. Target: {{data_warehouse_data_lake_ml_feature_store_reporting_db}}. Recommend: (1) pipeline architecture (Lambda / Kappa / Streaming-first), (2) technology stack for ingestion, processing, and storage, (3) data quality checks at each stage, (4) failure handling and retry strategy, (5) monitoring and alerting approach for pipeline health. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When building a new data pipeline or diagnosing why an existing one is unreliable or too slow.
Design for late-arriving data from day one — real-world data pipelines always receive out-of-order events, and retrofit solutions are expensive.
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