BigQuery BigLake是一个与BigQuery紧密集成的大规模数据存储解决方案，它可以提供更快的大规模数据分析。下面是几个重要的特点：

1. 模块化存储 BigLake将数据存储在模块化存储上，这意味着每个模块都可以单独管理和扩展。这有助于在存储大规模数据时更好地管理资源。

以下是创建模块并向其写入数据的Python示例：

from google.cloud import bigquery_storage_v1beta1

# TODO(developer): Set the ID of the project that contains the BigQuery dataset.
project_id = 'your-project-id'

# TODO(developer): Set the ID of the BigQuery dataset to write data to.
dataset_id = 'your_dataset_id'

client = bigquery_storage_v1beta1.BigQueryStorageClient()

# Set the table reference
table_ref = client.table_path(project_id, dataset_id, 'your_table_id')

# Set the write stream
stream = client.write_stream(Session(), table_ref)

# Open the stream
stream.open()

# Write rows to the stream
rows = [
    ("Row 1", 1),
    ("Row 2", 2),
    ("Row 3", 3)
]

stream.write_rows(rows)

# Close the stream
stream.close()

2. 高性能的数据读取 BigLake提供了高性能的数据读取能力，可以让用户更快地访问和查询大规模数据集。

以下是使用BigQuery Storage API读取BigLake中的数据的Python示例：

from google.cloud import bigquery_storage_v1beta1

# TODO(developer): Set the ID of the project that contains the BigQuery dataset.
project_id = 'your-project-id'

# TODO(developer): Set the ID of the BigQuery dataset that contains the table to be read.
dataset_id = 'your_dataset_id'

# TODO(developer): Set the ID of the BigQuery table to be read.
table_id =