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Sudnya
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bird-sql-full-database

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train · 273M rows

db_id stringclasses 7 values	table_name stringclasses 50 values	row_index int64 0 129M	row_data stringlengths 27 2.47k	split stringclasses 1 value
sales_in_weather	sales_in_weather	0	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 1, "units": 0}	train
sales_in_weather	sales_in_weather	1	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 2, "units": 0}	train
sales_in_weather	sales_in_weather	2	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 3, "units": 0}	train
sales_in_weather	sales_in_weather	3	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 4, "units": 0}	train
sales_in_weather	sales_in_weather	4	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 5, "units": 0}	train
sales_in_weather	sales_in_weather	5	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 6, "units": 0}	train
sales_in_weather	sales_in_weather	6	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 7, "units": 0}	train
sales_in_weather	sales_in_weather	7	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 8, "units": 0}	train
sales_in_weather	sales_in_weather	8	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 9, "units": 29}	train
sales_in_weather	sales_in_weather	9	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 10, "units": 0}	train
sales_in_weather	sales_in_weather	10	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 11, "units": 0}	train
sales_in_weather	sales_in_weather	11	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 12, "units": 0}	train
sales_in_weather	sales_in_weather	12	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 13, "units": 0}	train
sales_in_weather	sales_in_weather	13	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 14, "units": 0}	train
sales_in_weather	sales_in_weather	14	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 15, "units": 0}	train
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sales_in_weather	sales_in_weather	16	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 17, "units": 0}	train
sales_in_weather	sales_in_weather	17	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 18, "units": 0}	train
sales_in_weather	sales_in_weather	18	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 19, "units": 0}	train
sales_in_weather	sales_in_weather	19	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 20, "units": 0}	train
sales_in_weather	sales_in_weather	20	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 21, "units": 0}	train
sales_in_weather	sales_in_weather	21	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 22, "units": 0}	train
sales_in_weather	sales_in_weather	22	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 23, "units": 0}	train
sales_in_weather	sales_in_weather	23	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 24, "units": 0}	train
sales_in_weather	sales_in_weather	24	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 25, "units": 0}	train
sales_in_weather	sales_in_weather	25	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 26, "units": 0}	train
sales_in_weather	sales_in_weather	26	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 27, "units": 0}	train
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sales_in_weather	sales_in_weather	28	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 29, "units": 0}	train
sales_in_weather	sales_in_weather	29	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 30, "units": 0}	train
sales_in_weather	sales_in_weather	30	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 31, "units": 0}	train
sales_in_weather	sales_in_weather	31	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 32, "units": 0}	train
sales_in_weather	sales_in_weather	32	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 33, "units": 0}	train
sales_in_weather	sales_in_weather	33	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 34, "units": 0}	train
sales_in_weather	sales_in_weather	34	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 35, "units": 0}	train
sales_in_weather	sales_in_weather	35	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 36, "units": 0}	train
sales_in_weather	sales_in_weather	36	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 37, "units": 0}	train
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sales_in_weather	sales_in_weather	38	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 39, "units": 0}	train
sales_in_weather	sales_in_weather	39	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 40, "units": 0}	train
sales_in_weather	sales_in_weather	40	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 41, "units": 0}	train
sales_in_weather	sales_in_weather	41	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 42, "units": 0}	train
sales_in_weather	sales_in_weather	42	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 43, "units": 0}	train
sales_in_weather	sales_in_weather	43	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 44, "units": 0}	train
sales_in_weather	sales_in_weather	44	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 45, "units": 0}	train
sales_in_weather	sales_in_weather	45	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 46, "units": 0}	train
sales_in_weather	sales_in_weather	46	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 47, "units": 0}	train
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sales_in_weather	sales_in_weather	49	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 50, "units": 0}	train
sales_in_weather	sales_in_weather	50	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 51, "units": 1}	train
sales_in_weather	sales_in_weather	51	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 52, "units": 0}	train
sales_in_weather	sales_in_weather	52	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 53, "units": 0}	train
sales_in_weather	sales_in_weather	53	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 54, "units": 0}	train
sales_in_weather	sales_in_weather	54	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 55, "units": 0}	train
sales_in_weather	sales_in_weather	55	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 56, "units": 0}	train
sales_in_weather	sales_in_weather	56	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 57, "units": 0}	train
sales_in_weather	sales_in_weather	57	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 58, "units": 0}	train
sales_in_weather	sales_in_weather	58	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 59, "units": 0}	train
sales_in_weather	sales_in_weather	59	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 60, "units": 0}	train
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sales_in_weather	sales_in_weather	61	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 62, "units": 0}	train
sales_in_weather	sales_in_weather	62	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 63, "units": 0}	train
sales_in_weather	sales_in_weather	63	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 64, "units": 0}	train
sales_in_weather	sales_in_weather	64	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 65, "units": 0}	train
sales_in_weather	sales_in_weather	65	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 66, "units": 0}	train
sales_in_weather	sales_in_weather	66	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 67, "units": 0}	train
sales_in_weather	sales_in_weather	67	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 68, "units": 0}	train
sales_in_weather	sales_in_weather	68	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 69, "units": 0}	train
sales_in_weather	sales_in_weather	69	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 70, "units": 0}	train
sales_in_weather	sales_in_weather	70	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 71, "units": 0}	train
sales_in_weather	sales_in_weather	71	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 72, "units": 0}	train
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sales_in_weather	sales_in_weather	79	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 80, "units": 0}	train
sales_in_weather	sales_in_weather	80	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 81, "units": 0}	train
sales_in_weather	sales_in_weather	81	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 82, "units": 0}	train
sales_in_weather	sales_in_weather	82	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 83, "units": 0}	train
sales_in_weather	sales_in_weather	83	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 84, "units": 0}	train
sales_in_weather	sales_in_weather	84	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 85, "units": 0}	train
sales_in_weather	sales_in_weather	85	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 86, "units": 0}	train
sales_in_weather	sales_in_weather	86	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 87, "units": 0}	train
sales_in_weather	sales_in_weather	87	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 88, "units": 0}	train
sales_in_weather	sales_in_weather	88	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 89, "units": 0}	train
sales_in_weather	sales_in_weather	89	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 90, "units": 0}	train
sales_in_weather	sales_in_weather	90	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 91, "units": 0}	train
sales_in_weather	sales_in_weather	91	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 92, "units": 0}	train
sales_in_weather	sales_in_weather	92	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 93, "units": 0}	train
sales_in_weather	sales_in_weather	93	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 94, "units": 0}	train
sales_in_weather	sales_in_weather	94	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 95, "units": 0}	train
sales_in_weather	sales_in_weather	95	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 96, "units": 0}	train
sales_in_weather	sales_in_weather	96	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 97, "units": 0}	train
sales_in_weather	sales_in_weather	97	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 98, "units": 0}	train
sales_in_weather	sales_in_weather	98	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 99, "units": 0}	train
sales_in_weather	sales_in_weather	99	{"date": "2012-01-01", "store_nbr": 1, "item_nbr": 100, "units": 0}	train

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BIRD SQL Database with Complete Content

This dataset contains the complete BIRD SQL database content.

Statistics

Total databases: 80
Total rows: 366,787,649
Total chunks: 3660

Usage

from datasets import load_dataset

# Load database content
dataset = load_dataset("Sudnya/bird-sql-full-database", "database_content", streaming=True)

for example in dataset["train"]:
    print(f"Database: {example['db_id']}")
    print(f"Table: {example['table_name']}")
    print(f"Row data: {example['row_data']}")
    break

# Load table metadata  
metadata = load_dataset("Sudnya/bird-sql-full-database", "table_metadata", streaming=True)

for table in metadata["train"]:
    print(f"Table: {table['db_id']}.{table['table_name']}")
    print(f"Columns: {table['columns']}")
    break

Repository

https://huggingface.co/datasets/Sudnya/bird-sql-full-database

Downloads last month: 507

Size of downloaded dataset files:

7.49 GB

Size of the auto-converted Parquet files:

7.49 GB

Number of rows:

273,131,986