A 100-Task Benchmark of 7 Leading LLMs with Apache SeaTunnel AI CLI

Can AI Really Build Data Pipelines? A 100-Task Benchmark of 7 Leading LLMs with Apache SeaTunnel AI CLI

Apache SeaTunnel

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Author: Zhang Xin, Apache SeaTunnel Contributor Translator: Debra Chen

Abstract

Large language models (LLMs) are rapidly becoming part of modern data engineering workflows. They are increasingly used to understand natural language requirements, generate ETL configurations, validate configuration files, and assist with troubleshooting and fixing failures after execution.

For engineering teams, however, the real challenge is not getting a model to “generate a configuration.” The challenge is avoiding models that produce configurations that look correct but fail to run reliably in production.

In ETL scenarios, successfully generating a configuration — or even passing static validation — does not necessarily mean that a data pipeline can connect to real data sources, satisfy runtime prerequisites such as Change Data Capture (CDC), or complete end-to-end data synchronization. Selecting a model based solely on general-purpose benchmarks or a single successful generation can introduce repeated failures, manual troubleshooting, and unpredictable operational costs into production environments.

Based on the Apache SeaTunnel AI CLIproject, this article presents a layered benchmark of 100 ETL tasks across seven leading LLMs. Rather than evaluating configuration generation and static validation alone, the benchmark also verifies execution against real data environments. The results show that strong performance in static validation does notnecessarily translate into high runtime success rates.

Rather than ranking general-purpose LLMs, this benchmark proposes a practical evaluation methodology for AI-assisted ETL. Engineering teams should continuously evaluate and select models based on their own workload complexity, runtime success rate, error recovery capability, and overall operational cost, instead of relying on a single benchmark score.

Background: Apache SeaTunnel AI CLI and Why Accuracy Matters

Apache SeaTunnel is a top-level project of the Apache Software Foundation that provides data integration capabilities for batch processing, stream processing, and CDC workloads. Its ecosystem includes more than 100 connectors, covering JDBC, Kafka, Amazon S3, Hive, relational databases, messaging systems, and many other data platforms.

While this rich connector ecosystem enables SeaTunnel to support a wide range of integration scenarios, it also increases the complexity of configuration. A single connector may expose 20 to 50 configuration options, requiring users to understand parameter types, required fields, dependency relationships, execution modes, and the prerequisites of both upstream and downstream systems. In addition, SeaTunnel uses the HOCON configuration format, which further raises the learning curve for beginners working with complex pipelines.

One of the most common questions raised by community users can be summarized as follows:

“I know SeaTunnel can handle my data integration needs, but even after reading the documentation multiple times, I still can’t get the configuration file right.”

This challenge is exactly why SeaTunnel AI CLI was created.

Its goal is not simply to generate a piece of configuration text. Instead, it enables users to describe their data integration requirements in natural language — for example:

“Synchronize the orders table from MySQL to StarRocks using CDC, partitioned by the timestamp column.”

Based on this request, the AI CLI combines SeaTunnel’s connector knowledge, configuration rules, and runtime feedback to generate, validate, and iteratively refine the corresponding data pipeline configuration.

From a user experience perspective, the objective is to transform the configuration workflow from:

Read documentation → Assemble configuration parameters → Trial and error → Analyze logs → Manually fix errors

into:

Describe the requirement → Generate the configuration → Validate execution → Refine the configuration based on feedback

The ideal outcome is not to generate a HOCON file that merely looks reasonable, but to help users obtain a SeaTunnel configuration that is executable, verifiable, and maintainable on the first attempt whenever possible.

Achieving this goal requires far more than integrating an LLM API.

To support production-grade ETL workloads, the model must understand the semantics of more than 100 connectors, data type constraints, parameter dependencies, CDC prerequisites, and the composition of complex DAGs. At the same time, the AI CLI must convert SeaTunnel’s Java connector implementations, OptionRuledefinitions, configuration validation results, and runtime error messages into structured context that the model can understand and act upon.

Any hallucinated parameter, overlooked prerequisite, or incorrect repair strategy can cause the generated configuration to fail in a real deployment.

This makes the problem inherently complex and multidimensional.

The system must understand how existing Java connectors work while orchestrating a reliable Agent workflow in a Python CLI. It must leverage the model’s generation capabilities without relying on the model to “guess” the correct connector behavior. It must also iterate rapidly while validating every change against real data environments.

As a result, the most meaningful quality metric for the AI CLI is not whether a configuration can be generated or whether it passes static validation.

The metric that truly matters is accuracy:

Can the configuration generated by the model successfully complete a real data integration task under the specified data sources, destinations, and runtime conditions?

To answer this question, the remainder of this article evaluates different models using a three-layer validation framework, covering static configuration validation, CLI validation, and real execution. Based on these results, we further discuss model selection strategies and future engineering improvements for AI-assisted ETL in production environments.

Evaluation Methodology: A Three-Layer Validation Framework from Static Checks to Real Execution

Traditional benchmarks for configuration generation often stop at evaluating syntax correctness, text similarity, or manual spot checks. However, for a data integration platform like Apache SeaTunnel, a configuration that looks correct does not necessarily mean the corresponding data pipeline can run successfully in a real production environment.

For this reason, our benchmark does not treat “generating a HOCON configuration” as the end goal. Instead, every model-generated configuration passes through a progressively stricter validation pipeline: we first verify the basic configuration structure, then validate it against SeaTunnel CLI rules and connector constraints, and finally execute it in a Dockerized environment with real data services.

These three stages are referred to as L1 Static Validation, L2 CLI Validation, and L3 Runtime Validation.

Benchmark Tasks and Coverage

The benchmark consists of 100 Apache SeaTunnel ETL tasks, grouped into three tiers based on task complexity.

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The benchmark covers a wide range of scenarios, including batch ETL, CDC, data format processing, field mapping, transformation logic, and complex DAG workflows. The runtime environment includes components such as MySQL, PostgreSQL, Kafka, ClickHouse, Elasticsearch, MinIO, Doris, and StarRocks.

Each benchmark task includes:

A natural language description of the data integration requirement

The expected source-to-target data flow

The required runtime environment

Success criteria used to determine whether the task has been completed correctly

The overall evaluation workflow is shown below.

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L1: Static Configuration Validation

L1 evaluates whether the model can generate a structurally valid Apache SeaTunnel configuration from a natural language request.

This stage verifies the following:

Whether the HOCON configuration can be parsed successfully

Whether the required sections — such as env, source, transform, and sink—are present

Whether connector names, required fields, and field types satisfy the basic configuration requirements

Whether the configuration passes the initial set of static validation rules

L1 answers a straightforward question:

Can the model generate a SeaTunnel configuration that is structurally correct?

This validation stage is fast and well suited for large-scale benchmarking and routine regression testing.

However, its limitations are equally clear.

A HOCON file may be syntactically valid and contain all the required sections, yet still fail because of incorrect connector parameters, invalid parameter combinations, missing runtime prerequisites, failed connections to external systems, or unsatisfied CDC requirements.

Passing L1 simply indicates that the configuration looks valid — it does not guarantee that it can actually run.

L2: CLI and Rule-Based Validation

Building on L1, the L2 stage introduces SeaTunnel CLI validation through dry-run or the --check option, together with connector-specific validation rules defined by OptionRule, parameter constraints, and DAG validation.

Compared with L1, L2 focuses on whether the configuration complies with the execution rules that can be verified before runtime.

These checks include:

Whether connector parameters are complete and free from invalid combinations

Whether the relationships among the source, transform, and sink components are valid

Whether the DAG structure and execution mode are correctly configured

Whether known constraints related to CDC, data formats, schemas, or checkpoints are satisfied

L2 answers a different question:

Beyond being syntactically correct, does the configuration comply with SeaTunnel’s execution rules and connector validation requirements?

This stage catches many configurations that appear reasonable from a textual perspective but violate connector rules.

For example, a model may correctly generate a MySQL source and a StarRocks sink, yet omit a mandatory connector option or use an incompatible parameter combination for a CDC pipeline.

Even so, L2 cannot fully replace runtime validation.

Many issues only become visible after connecting to external services, reading real data, or executing the complete pipeline topology.

L3: Runtime Validation in a Dockerized Environment

L3 is the core of this benchmark.

For every configuration that passes the previous two stages, we launch a complete test environment using Docker Compose, including data sources, messaging systems, target databases or storage systems, and the Apache SeaTunnel runtime itself.

The generated configuration is then used to submit a real SeaTunnel job, and the benchmark verifies whether the expected data synchronization task completes successfully.

For CDC workloads, successful execution depends on many runtime prerequisites, including database binlogs or logical replication, user permissions, publications, server-idsettings, checkpoint configuration, and connector version compatibility.

For complex DAG workflows, only a real execution can verify whether data flows correctly through multiple sources, transformations, and sinks before being written to the target systems.

The L3 workflow consists of six steps:

Start the required Docker Compose environment.

Prepare source-side test data, CDC state, or messaging data.

Submit the Apache SeaTunnel job using the model-generated configuration.

Monitor the job startup, execution, and completion status.

Verify that the expected data has been written correctly to the target system.

Preserve execution logs, error messages, and repair records for every failed task.

As a result, an L3 s

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