Data Pipeline Patterns

Data Pipeline Patterns — Team Conventions

Claude already knows validation, error handling, and retry patterns. This covers our team's specific stage structure and conventions.

When to Activate

• Building or modifying data pipeline stages
• Debugging pipeline failures (empty data, schema mismatches)
• Reviewing pipeline code

Standard Stage Structure

Every pipeline stage follows this pattern:

def main(argv=None):
    args = parse_args(argv)

    # 1. Load input
    input_data = load_json_file(Path(args.file))
    if input_data is None:
        sys.exit(1)

    # 2. Validate input
    if not validate_json_structure(input_data, ["required_key"], "Input file"):
        sys.exit(1)

    # 3. Process
    result = process(input_data)

    # 4. Save output with metadata
    output = {
        "metadata": {
            "source_file": str(args.file),
            "generated_at": datetime.now().isoformat(),
            "items_processed": len(result),
        },
        "data": result
    }
    save_json_file(output, output_path)

Key rules: Each stage is independently runnable (CLI + importable). Validate input before processing. Include metadata in every output. Fail fast on invalid input. Checkpoint every N items on long operations.

JSON Data File Conventions

• All output files: metadata dict + data (list or dict)
• Metadata always has: source_file, generated_at, count fields
• Use _ prefix for derived/computed fields
• Dates as ISO 8601 strings (2026-03-15T10:00:00)
• Save with indent=2 and ensure_ascii=False

Debugging Pipeline Failures

Anti-Patterns

• Processing without validation — always check input before transforming
• Swallowing errors — never except: pass in pipeline stages
• Hardcoded file paths — use arguments or config, never absolute paths
• No logging — every stage should log what it's doing and how many items
• Monolithic stages — if a stage does 3 things, split it into 3 stages
• Missing metadata — every output file should be self-describing