After acquiring and preprocessing potentially terabytes or even petabytes of text data, the next engineering step involves effectively storing, organizing, and accessing this massive collection. This chapter addresses the infrastructure and techniques required to manage datasets at the scale necessary for large language model training.

We will cover practical considerations such as:

• Choosing appropriate data storage formats (like text, Apache Arrow, or Parquet).
• Utilizing distributed file systems such as HDFS or cloud object storage.
• Implementing data indexing for efficient retrieval.
• Establishing dataset versioning practices for reproducibility.
• Designing streaming data loaders that feed data effectively into distributed training processes.