text: from datetime import datetime, timedelta
  
  import numpy as np
  import pandas as pd
  from definitions import (
      benchmark_feature_service,
      benchmark_feature_views,
      driver,
      driver_hourly_stats_view,
      entity,
      transformed_conv_rate,
  )
  
  from feast import FeatureStore
  
  print("Running materialize.py")
  
  # Fill our temporary data source
  start = datetime.now() - timedelta(days=10)
  
  df = pd.DataFrame()
  df["driver_id"] = np.arange(1000, 1010)
  df["created"] = datetime.now()
  df["conv_rate"] = np.arange(0, 1, 0.1)
  df["acc_rate"] = np.arange(0.5, 1, 0.05)
  df["avg_daily_trips"] = np.arange(0, 1000, 100)
  
  # some of rows are beyond 7 days to test OUTSIDE_MAX_AGE status
  df["event_timestamp"] = start + pd.Series(np.arange(0, 10)).map(
      lambda days: timedelta(days=days)
  )
  
  # Store data in parquet files. Parquet is convenient for local development mode. For
  # production, you can use your favorite DWH, such as BigQuery. See Feast documentation
  # for more info.
  df.to_parquet("driver_stats.parquet")
  
  
  # For Benchmarks
  # Please read more in Feast RFC-031
  # (link https://docs.google.com/document/d/12UuvTQnTTCJhdRgy6h10zSbInNGSyEJkIxpOcgOen1I/edit)
  # about this benchmark setup
  def generate_data(num_rows: int, num_features: int, destination: str) -> pd.DataFrame:
      features = [f"feature_{i}" for i in range(num_features)]
      columns = ["entity", "event_timestamp"] + features
      df = pd.DataFrame(0, index=np.arange(num_rows), columns=columns)
      df["event_timestamp"] = datetime.utcnow()
      for column in features:
          df[column] = np.random.randint(1, num_rows, num_rows)
  
      df["entity"] = "key-" + pd.Series(np.arange(1, num_rows + 1)).astype(
          pd.StringDtype()
      )
  
      df.to_parquet(destination)
  
  
  generate_data(10 ** 3, 250, "benchmark_data.parquet")
  
  
  fs = FeatureStore(".")
  fs.apply(
      [
          driver_hourly_stats_view,
          transformed_conv_rate,
          driver,
          entity,
          benchmark_feature_service,
          *benchmark_feature_views,
      ]
  )
  
  now = datetime.now()
  fs.materialize(start, now)
  print("Materialization finished")
  