Text-to-SQL for Enterprise Data Analytics

• URL: http://arxiv.org/abs/2507.14372v1
• Date: Fri, 18 Jul 2025 21:39:17 GMT
• Title: Text-to-SQL for Enterprise Data Analytics
• Authors: Albert Chen, Manas Bundele, Gaurav Ahlawat, Patrick Stetz, Zhitao Wang, Qiang Fei, Donghoon Jung, Audrey Chu, Bharadwaj Jayaraman, Ayushi Panth, Yatin Arora, Sourav Jain, Renjith Varma, Alexey Ilin, Iuliia Melnychuk, Chelsea Chueh, Joyan Sil, Xiaofeng Wang,
• Abstract summary: We present insights from building an internal bot that enables LinkedIn's product managers, engineers, and operations teams to self-serve data insights from a large, dynamic data lake.<n>Our approach features three components. First, we construct a knowledge graph that captures up-to-date semantics by indexing database metadata, historical query logs, wikis, and code.<n>Second, we build a Text-to-one clustering agent that retrieves and ranks context from the knowledge graph, writes a query, and automatically corrects hallucinations and syntax errors.
• Score: 6.08835924526836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The introduction of large language models has brought rapid progress on Text-to-SQL benchmarks, but it is not yet easy to build a working enterprise solution. In this paper, we present insights from building an internal chatbot that enables LinkedIn's product managers, engineers, and operations teams to self-serve data insights from a large, dynamic data lake. Our approach features three components. First, we construct a knowledge graph that captures up-to-date semantics by indexing database metadata, historical query logs, wikis, and code. We apply clustering to identify relevant tables for each team or product area. Second, we build a Text-to-SQL agent that retrieves and ranks context from the knowledge graph, writes a query, and automatically corrects hallucinations and syntax errors. Third, we build an interactive chatbot that supports various user intents, from data discovery to query writing to debugging, and displays responses in rich UI elements to encourage follow-up chats. Our chatbot has over 300 weekly users. Expert review shows that 53% of its responses are correct or close to correct on an internal benchmark set. Through ablation studies, we identify the most important knowledge graph and modeling components, offering a practical path for developing enterprise Text-to-SQL solutions.

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