Preparing data

Efficiently handling large datasets is paramount. One of the most effective ways to manage and process such data is through parallel programming environments. Apache Spark stands out as a powerful tool for this purpose, offering robust capabilities for data processing, analysis, and machine learning. Specifically, PySpark, the Python API for Spark, simplifies these tasks with its easy-to-use interface. This section explores how to import collected data, which is stored in Parquet format, into PySpark for parallel processing in an effort to fine-tune the LLM.

PySpark is an interface for Apache Spark, which allows for distributed data processing across clusters. Spark’s in-memory computation capabilities make it significantly faster for certain operations compared to other big data technologies. Parquet, on the other hand, is a columnar storage file format that is optimized for use with big data processing frameworks. It offers efficient data compression and...