KnightHacks SAP Decryption Project - Team Kinger

Inspiration

The inspiration for our team of first-time hackathon participants was the interesting challenge of tackling programing a decoder for an SAP File. We all had vague ideas of what a SAP File was and what it did but this project

What it does

This program is a Python-based tool designed to read, parse, and export SAP binary data files. It can automatically detect whether input files are compressed with gzip, LZ4, or Zstandard and decompress them accordingly. Once decompressed, the program interprets each record based on a predefined schema that specifies field names, data types, and lengths, supporting common SAP types like CHAR, NUMC, DATE, INT4, and packed decimal (DEC). It safely handles incomplete or corrupted records, logging warnings without crashing, and provides progress feedback using a console-based progress bar. Parsed data is exported to CSV files, with each field’s value, data type, and byte length recorded, making it easy to analyze or integrate with other systems. The program can process a single file or multiple files in parallel using multi-threading, improving performance for large datasets. Overall, it automates the extraction of structured data from SAP binary files into a standard, human-readable format for reporting, analytics, or further processing.

How we built it

This program was built using Python 3 and leverages a combination of standard libraries and third-party packages to efficiently parse and export SAP binary data. Core functionality relies on Python modules like os, gzip, csv, struct, and concurrent.futures for file handling, decompression, binary parsing, and multi-threading. Optional support for LZ4 and Zstandard compression was added using the lz4.frame and zstandard libraries. User-friendly features, such as progress tracking, were implemented with tqdm, while robust error handling uses Python’s traceback module. The SAP-specific parsing logic—including decoding CHAR, NUMC, DATE, INT4, and packed decimal fields—was designed to safely handle incomplete or corrupted records. This code and its design were conceptually assisted by OpenAI’s ChatGPT, providing guidance on structure, error handling, and parsing strategies, making it easier to handle SAP binary files in Python efficiently.

Challenges we ran into

The biggest challenges our group ran into were understanding the requirements of an SAP decoder and ensuring that the parsed output was accurate and meaningful. SAP binary files use specialized data types and fixed-length fields, which made it difficult to interpret raw bytes correctly without producing corrupted or misleading values. Another challenge was handling compressed files, such as gzip, LZ4, and Zstandard, and ensuring they could be decompressed reliably before parsing. Additionally, we had to implement robust error handling to prevent incomplete or corrupted records from causing the program to crash or produce garbage output. Through careful testing, debugging, and guidance from OpenAI’s ChatGPT, we were able to correctly decode the data and generate clean, structured CSV files.

What we learned

We gained a deeper understanding of SAP data structures, handle binary data in Python, unpacking integers, and decoding bytes safely into human-readable formats. Working with compressed files taught us how to detect and decompress multiple formats specifically gzip, LZ4, and Zstandard

Built With

• csv
• gzip
• lz4
• openai
• os
• python
• struct
• zstandard
• zstd