Title: Revolutionizing Data Documentation with OpenMetadata and Large Language Models

Introduction:
In today's data-driven world, managing vast amounts of data efficiently is crucial for businesses to thrive. However, the complexity of handling numerous tables scattered across various databases presents a significant challenge. In this article, we explore how OpenMetadata and Large Language Models (LLMs) offer innovative solutions to streamline data management and documentation processes.

The Challenge of Data Management:
Managing tens of thousands of tables across multiple databases and systems can be overwhelming for businesses. The lack of centralized documentation and collaboration tools makes it difficult for teams to efficiently utilize and understand data assets. This challenge underscores the need for a robust platform that provides a unified view of data assets and facilitates collaboration among team members.

Introducing OpenMetadata:
OpenMetadata emerges as a promising solution to address the challenges of data management. This platform offers a comprehensive metadata management solution, allowing organizations to create a centralized repository for documenting and discovering data assets. By leveraging OpenMetadata, businesses can establish a unified platform for collaboration, documentation, and data governance.

The Role of Large Language Models:
Large Language Models, such as GPT and YandexGPT, play a transformative role in automating the documentation process. These advanced natural language processing models can interpret and contextualize data, enabling the generation of comprehensive documentation for tables and columns. By analyzing the structure, relationships, and content of data assets, LLMs generate descriptive narratives, field definitions, and usage guidelines automatically.

Case Study: Revolutionizing Data Documentation at a Retail Company:
Let's delve into a real-world example of how OpenMetadata and LLMs revolutionized data documentation at a retail company located in Eastern Europe. This company faced challenges due to the diverse nature of its data, including mixed language schemas and complex column names.

The Solution:
To address these challenges, the company implemented OpenMetadata and integrated LLMs into its data documentation process. Leveraging the OpenMetadata SDK for Python and Java, the team developed an Airflow DAG that extracted table schemas from OpenMetadata and passed them to LLMs for explanation.

Comparing LLMs: GPT vs. YandexGPT:
In this project, the team sought to evaluate the capabilities of different LLMs. They chose GPT as the primary model and YandexGPT as a comparative tool due to its purported better support for Slavic languages. The LangChain platform facilitated seamless integration with YandexGPT, enabling the team to assess its performance alongside GPT.

Results and Impact:
The implementation of OpenMetadata and LLMs yielded significant results for the retail company. Within a few hours, the Airflow DAG documented tens of thousands of tables and columns, providing comprehensive and accurate documentation. The automation of the documentation process not only saved time but also improved data accessibility and understanding for team members.

Conclusion:
The combination of OpenMetadata and Large Language Models offers a transformative solution for data management and documentation. By centralizing data assets and automating the documentation process, businesses can enhance collaboration, ensure data quality, and unlock the full potential of their data assets.