In the previous article titled “How AI is Changing the Data Engineering Lifecycle,” I explored how AI technologies are reshaping the data engineering lifecycle by automating ingestion, enhancing transformation logic, improving data quality, and elevating monitoring capabilities. In case you missed reading it, click here.

In this article, I focus on the most transformative AI advancement so far – Large Language Models (LLMs) – and their role in shaping modern-day data engineering.

How are LLMs being used in data engineering?

LLMs function as intelligent assistants, supporting faster development, deeper data understanding, and improved documentation. Below are the major areas where LLMs are making an impact:

1. Natural language to SQL / ETL code
   LLMs translate business questions or intent in natural language into SQL queries or ETL logic.
   Example: “Get total revenue by region for the last quarter” → LLM generates correct SQL or PySpark code for the relevant tables.

   Benefits:
   

   • Accelerates development and reduces dependency on SQL experts
   • Empowers analysts and non-technical users to self-serve
   • Reduces backlogs for engineering teams
2. Schema and relationship understanding
   LLMs quickly infer the meaning of poorly documented schemas, suggest joins, and explain relationships between datasets.
   Example: If you ask an LLM to explain how orders, products, and customers are related, it will generate a logical entity relationship mapping.

   Benefits:
   

   • Enhances onboarding and collaboration
   • Supports automated lineage detection
   • Useful in reverse-engineering legacy systems
3. Metadata and documentation generation
   LLMs automate the generation of column descriptions, tag sensitive data, identify PII, and provide classifications for data assets.
   Example: An LLM identifies a column named Social Security Number (SSN) as sensitive and recommends masking or classification as PII.

   Benefits:
   

   • Reduces manual documentation effort
   • Improves data catalog quality
   • Helps maintain compliance standards

Challenges and limitations

Despite the promise of LLMs, several challenges must be addressed before they can be safely and reliably integrated into production workflows.

1. Hallucinations and inaccuracies
   LLMs can produce incorrect or misleading SQL or transformation logic when context is insufficient.

   Risks:
   

   • Faulty joins or incorrect aggregations
   • Business logic misinterpretation
   • Production errors and unreliable output

   Mitigation:
   

   • Manual reviews and validation frameworks
   • Embedding test generation and data profiling
     
2. Governance and compliance concerns
   LLMs are unaware of data access policies or enterprise governance frameworks unless explicitly integrated.

   Risks:
   

   • Unauthorized access to restricted data
   • Violation of compliance and privacy policies

   Mitigation:
   

   • Connect LLMs to metadata and access control systems
   • Use policy-aware prompt engineering
3. Absence of built-in validation
   LLMs do not verify the accuracy, efficiency, or safety of their outputs.

   Risks:
   

   • Long-running or inefficient code
   • Unhandled edge cases or data anomalies

   Mitigation:
   

   • Implement AI output validation layers
   • Combine LLM-generated code with monitoring and profiling tools

Closing Thoughts

LLMs represent a new frontier in augmenting data engineering, accelerating development, improving understanding, and automating documentation. However, their power comes with new responsibilities.

For now, LLMs serve best as co-pilots, providing recommendations, suggestions, and automation that require human oversight. Teams must build a thoughtful integration strategy that includes governance, validation, and feedback loops to harness LLMs safely and effectively.

The future will likely bring tighter integrations between LLMs and metadata platforms, cataloging tools, and orchestration engines, enabling a truly AI-augmented data engineering environment