Datasets:

n2d
/

worldcup-pulse-data

Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      BatchSize must be greater than 0, got 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 208, in _generate_tables
                  parquet_fragment.to_batches(
                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      batch_size=batch_size,
                      ^^^^^^^^^^^^^^^^^^^^^^
                  ...<3 lines>...
                      fragment_readahead=0,
                      ^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "pyarrow/_dataset.pyx", line 1648, in pyarrow._dataset.Fragment.to_batches
                File "pyarrow/_dataset.pyx", line 3792, in pyarrow._dataset.Scanner.from_fragment
                  _populate_builder(builder, columns=columns, filter=filter,
                File "pyarrow/_dataset.pyx", line 3574, in pyarrow._dataset._populate_builder
                  check_status(builder.BatchSize(batch_size))
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: BatchSize must be greater than 0, got 0

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WorldCup Pulse Data

This Hugging Face Dataset repository is the single source of truth for WorldCup Pulse Lakehouse data. The GitHub code repo never commits generated data; GitHub Actions uploads this lakehouse output to this dataset repo.

Lakehouse layout

bronze/*.parquet     # normalized raw API snapshots
silver/*.parquet     # cleaned canonical entities and events
gold/*.parquet       # dashboard-ready marts
state/last_run.json  # incremental state
logs/*.csv           # pipeline and quality audit logs

Gold marts

Core tournament marts:

kpi_summary.parquet: tournament-level KPIs including matches played, goals, avg goals, biggest win, cards, penalties, VAR goals, and matches remaining.
goals_by_matchday.parquet: goals and match counts by matchday.
goals_by_minute_bucket.parquet: goal distribution across 15-minute buckets.
host_cities.parquet: stadium, city, country, coordinates, and hosted match count.
team_radar_stats.parquet: attack, defense, possession, passing, and discipline score by team.
team_key_metrics.parquet: xG, shots/match, possession, pass accuracy, goals for/against, cards, clean sheets, and set-piece goals.
top_players.parquet: player attacking and contribution leaderboard.
team_table.parquet: team-level aggregate table.

Expanded BI marts used by the updated Overview Space:

matches.parquet: fixture/result table with match IDs, teams, flags, scores, xG, venue, city, status, and kickoff metadata.
group_standings.parquet: live group table with played, W-D-L, goals for/against, goal difference, points, and qualification status.
match_events.parquet: goal/card-style events with minute, half, team, player, assist player, and pitch coordinates.
substitutions.parquet: substitution timing and player changes by match.
lineups.parquet: starting XI-style lineup rows by match and team.
goalkeepers.parquet: goalkeeper saves, save percentage, penalties saved, clean sheets, and goals conceded.
match_player_stats.parquet: per-player-per-match form, ratings, distance, speed, passing, tackles, and interceptions.

Operational logs

logs/pipeline_runs.csv: run ID, start/end timestamp, status, row counts, and error message.
logs/quality_checks.csv: quality checks by layer/table/check name/status.

license: mit

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