Why We Need Data Engineering Benchmarks for LLMs

The article discusses the need for a better evaluation method for tools like Copilot and GPT-based copilots that are designed to assist with data engineering tasks. The author argues that while SWE-bench provides a framework for software engineering, data engineering is often overlooked, with no tailored benchmarks or precise ways to measure effectiveness. The author proposes a DE-bench that simulates real-world data engineering workflows, evaluating language learning models (LLMs) on their ability to solve practical, pipeline-oriented problems.

The proposed DE-bench would include task categories such as data ingestion, data transformation, pipeline orchestration, and schema management. Evaluation would be based on functional correctness, edge case handling, performance, and maintainability. The author believes that this would provide a structured, objective framework for assessing LLMs on real-world data engineering tasks, ensuring that these tools are reliable, efficient, and robust. The author invites interested parties to collaborate on this initiative.

Key takeaways:

Data engineering tasks are fundamentally different from software engineering tasks, focusing more on data, pipelines, and the quality, reliability, and scalability of data workflows.
Current evaluation methods for tools like Copilot and GPT-based copilots are lacking in the data engineering field, with no tailored benchmarks or precise ways to gauge their effectiveness.
A proposed DE-bench would simulate real-world data engineering workflows, evaluating LLMs on their ability to solve practical, pipeline-oriented problems.
The DE-bench would provide a structured, objective framework for assessing LLMs on real-world data engineering tasks, ensuring that these tools are reliable, efficient, and robust.

Why We Need Data Engineering Benchmarks for LLMs

Key takeaways:

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