Unidumptoreg.24 !!install!!

1. Purpose and Functionality

4. Incident summary (observed failures)

  • Owner: Data Engineering
    1. Determine file type: run file command or check magic bytes.
    2. If JSON/NDJSON: use ijson or jq to stream, extract fields into columns.
    3. If CSV: read in chunks (pandas.read_csv with chunksize).
    4. If binary/protobuf: obtain schema, use protobuf parser.
    5. Normalize nested arrays by exploding or aggregating (counts, means).
    6. Convert categorical fields: frequency encoding for high-cardinality, one-hot for low.
    7. Impute missing values: median for numeric, mode or “missing” category for categorical.
    8. Scale numerical features if needed (StandardScaler or robust scaler).
    9. Save outputs: write compressed Parquet and a small sample CSV.

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