{
    "action": "final",
    "content": "**raw.Iris** has 10 columns and 150 rows, one row per measured iris flower. Id is unique (150 distinct values, 1–150), and the four measurements and Species have no nulls (step-2 profile).\n\n**Data columns**\n\n| Column | Type | Meaning |\n|---|---|---|\n| Id | BIGINT | Row identifier for the specimen (1–150). |\n| SepalLengthCm | DOUBLE | Sepal length in centimetres (4.3–7.9). |\n| SepalWidthCm | DOUBLE | Sepal width in centimetres (2.0–4.4). |\n| PetalLengthCm | DOUBLE | Petal length in centimetres (1.0–6.9). |\n| PetalWidthCm | DOUBLE | Petal width in centimetres (0.1–2.5). |\n| Species | VARCHAR | The iris species label, used as the classifier. There are 3 values (Iris-setosa, Iris-versicolor, Iris-virginica), 50 rows each. |\n\nThe four measurement columns are the dataset's measures. Their units are centimetres, taken from the column names. The per-species averages (gold table `Iris_by_Species`) show petal size separating the species most clearly. Mean petal length is about 1.46 cm for setosa, 4.26 for versicolor and 5.55 for virginica.\n\n**Ingestion metadata columns**\n\nThese were added by the pipeline and do not describe the flowers. I did not query their values, and the types and meanings below come from the dataset card and column names.\n\n| Column | Type | Meaning |\n|---|---|---|\n| _ingestion_timestamp | TIMESTAMP | When the row was loaded. |\n| _batch_id | VARCHAR | Identifier of the load batch. |\n| _source_file | VARCHAR | File the row came from. |\n| _source_system | VARCHAR | System the data originated from. |"
}