Why We Need Data Engineering Benchmarks for LLMs

Tools like Copilot and GPT-based copilots promise to reduce the repetitive burden of data engineering tasks, suggest code, and even debug complex pipelines. But how do we measure whether they’re actually good at this? Frankly, the industry is lagging behind when it comes to evaluation methods. While SWE-bench offers a framework for software engineering, data engineering is just left out—no tailored benchmarks, no precise way to gauge their effectiveness. It’s time to change that.

SWE-bench evaluates LLMs on real-world software engineering tasks by using GitHub issue–pull request pairs from popular repositories. It provides:

1. Inputs: An issue description and the state of the codebase before the issue was resolved.

2. Objective: The LLM generates a patch (code fix).

3. Evaluation: Patches are tested against:

This setup evaluates an LLM’s ability to:

• Understand code and requirements.

• Fix bugs or implement features.

• Maintain stability in the existing codebase.

While this works well for SWE, data engineering is fundamentally different:

• The focus is on data rather than application logic.

• Pipelines, not individual features, are the core unit of work.

• Success is measured by the quality, reliability, and scalability of data workflows, not just correctness of code.

In short, software engineers may solve complicated problems, but data engineers solve complex systems-level problems involving constant data motion, quality maintenance, and continuous evolution. Treating these disciplines as equivalent is doing data engineering a disservice.

• DE deals with raw, messy, and large-scale data that must be cleaned, transformed, and made accessible.

• i.e. handling data quality, schema evolution, and compliance, not just code correctness.

• Data engineers often use specialized tools like Apache Spark, Kafka, Airflow, and Snowflake, rather than general-purpose programming frameworks.

• DE focuses on automating and orchestrating multi-step workflows, which requires understanding task dependencies, scheduling, and retries.

• Edge-cases! DE must handle schema drift, missing values, malformed records, and outliers, which are rarely part of SWE workflows.

In essence, DE is less about writing application logic and more about making data usable, accessible, and reliable. Data engineers don’t just write code. They manage workflows that operate across multiple systems, handle changing requirements, and scale with data growth. A DE benchmark needs to reflect these realities.

The jobs-to-be-done in data engineering revolve around building and maintaining the infrastructure and pipelines that enable data-driven decision-making. Key JTBD include:

• Data Ingestion. Extracting data from various sources (APIs, files, databases). Handling structured, semi-structured, and unstructured data.

• Data Transformation. Cleaning, normalizing, and enriching data for analytics. Writing ETL/ELT pipelines.

• Pipeline Orchestration. Automating multi-step workflows with tools like Airflow or Prefect. Scheduling tasks, managing dependencies, and ensuring retries.

• Schema Management. Handling schema evolution and migrations without breaking downstream systems.

• Data Quality and Validation. Implementing checks for missing, duplicate, or inconsistent data. Generating data quality reports.

• Optimization and Scaling. Tuning pipelines for performance and cost efficiency.

• Monitoring and Debugging. Adding logging, alerts, and metrics for proactive pipeline monitoring.

One might argue that text-to-SQL benchmarks are a step toward evaluating LLMs for data engineering tasks. While useful, text-to-SQL falls far short of the needs of DE benchmarks for several reasons:

• Text-to-SQL only handles querying structured data. Data engineering is so much more: it involves transforming, orchestrating, and making sense of chaotic, mixed-format data.

• Lack of Pipeline Context. DE isn’t more than single queries. it’s about creating end-to-end workflows that deliver business value.

• Handling real-world problems like schema drift or malformed records

• Data engineers work with Spark, Airflow, dbt—not just databases. Generating a SQL query is child’s play compared to orchestrating a complex Spark job across petabytes of data.

• Reliability, scalability, and optimization are make-or-break factors for DE. These are entirely missed by simple SQL benchmarks.

In short, text-to-SQL is just one piece of the DE puzzle. Evaluating DE copilots requires a broader, pipeline-focused benchmark.

A DE-bench would simulate real-world DE workflows, evaluating LLMs on their ability to solve practical, pipeline-oriented problems. Here’s how it might look:

1. Dataset Sources:

   • Use public datasets (e.g., NYC Taxi data, Kaggle datasets).

   • Generate synthetic data to simulate edge cases (e.g., missing values, schema drift).

2. Task Categories:

   • Data Ingestion: Load raw data from APIs or files into a database or data lake.

   • Data Transformation: Normalize data formats, remove duplicates, and compute aggregates.

   • Pipeline Orchestration: Create an Airflow DAG for a multi-step ETL pipeline.

   • Schema Management: Migrate data between schemas safely.

3. Evaluation:

   • Functional Correctness: Does the pipeline produce the expected output?

   • Edge Case Handling: Can the solution handle schema drift or malformed records?

   • Performance: Is the solution scalable and efficient?

   • Maintainability: Is the generated code modular and easy to debug?

4. Automated Validation:

   • Use unit and integration tests to validate task outputs.

   • Monitor performance metrics (e.g., runtime, throughput).

5. Output:

1. For LLM Developers:

   • Identify gaps in current models for DE-specific tasks.

   • Drive improvements in handling schema evolution, edge cases, and real-time workflows.

2. For Data Engineers:

3. For Organizations:

A DE-bench would provide a structured, objective framework for assessing LLMs on real-world DE tasks, ensuring that these tools are reliable, efficient, and robust. It’s time we hold DE copilots to the same high standards as their SWE counterparts.

If this sounds interesting to you, would love to collaborate with you – shoot me an email amrutha@structuredlabs.com.

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