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Where generative AI fits in ETL work
ETL means extract, transform, load: data is taken from source systems, transformed, then loaded into a destination. Current vendor-documented AI features assist with parts of that work rather than eliminating the pipeline or the engineering decisions around it.
For example, AWS documents Amazon Q data integration in AWS Glue as a way to ask natural-language questions about Glue and data integration, generate PySpark ETL scripts, and get help diagnosing job failures. Google documents a Data Engineering Agent API that uses natural-language prompts to build, modify, and manage pipelines for loading and processing data in BigQuery. These are examples tied to specific platforms, not proof that every data tool offers the same capabilities.
What engineers can use it for
- Understand a task: Ask questions about an integration service or describe the data movement you want to implement.
- Start or revise pipeline code: Use a prompt to generate a script or make changes to a pipeline, then inspect the result against the actual schema and requirements.
- Troubleshoot: Request help interpreting job failures and identifying possible fixes; confirm the cause in logs and with a controlled test.
These uses can change how a task begins, but the sources do not establish a general productivity, accuracy, or cost improvement. Treat an LLM response as assistance, not as evidence that a pipeline works correctly.
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Two documented platform examples
| Platform example | Documented scope | Important qualification |
|---|---|---|
| Amazon Q data integration in AWS Glue | Natural-language questions about Glue and data integration, PySpark ETL script generation, and troubleshooting assistance. | AWS documents generated-code support for the PySpark kernel and advises reviewing the script before execution. |
| Google Cloud Data Engineering Agent API | An A2A-based API that accepts natural-language prompts to build, modify, and manage BigQuery loading and processing pipelines. | Google describes it as early-stage and advises validating its output before use. |
The two examples have different scopes and should not be read as equivalent products. When assessing a feature, check whether it works with your existing platform, destination, and engine; whether it answers questions, generates or edits code, or helps troubleshoot; and what controls apply to data access and generated output.
Generated pipeline code needs engineering review
A generated script is a draft, not tested production code. AWS advises: “Review the generated script before running it to ensure accuracy.” It also recommends specific prompts and checking generated code for errors and vulnerabilities. Google warns that its early-stage agent can produce plausible but factually incorrect output and recommends validation.
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- Specify the task: Include the source and destination, intended transformations, relevant schema details, and constraints in the prompt. Avoid exposing secrets or sensitive records.
- Inspect the result: Check joins, filters, null handling, data types, schema changes, error handling, and any assumptions the code makes about the source.
- Review security and permissions: Look for unsafe handling of credentials or personal information, and ensure the job has only the access it needs.
- Test in the target environment: Use representative data and verify output, failure behavior, and resource use before allowing a production run.
- Keep normal change controls: Track revisions and have the appropriate engineer approve deployment, just as for manually written pipeline code.
ETL, ELT, and EL are different choices
Generative AI does not decide whether a workflow should transform data before or after loading. That architecture choice depends on the workload, destination, and existing systems.
- ETL: Extract data, transform it, then load the prepared result into its destination.
- ELT: Extract data, load it, then transform it in the destination platform. Google generally recommends ELT for most BigQuery customers; ETL can be useful when pre-load transformations already exist or when reducing BigQuery resource use is a goal.
- EL: Extract and load first, with transformation later. Microsoft describes this pattern in some RAG workflows, where content may be stored before steps such as chunking or image extraction.
For broader context on modern batch and streaming workloads, see Google Cloud’s ETL overview. The sequence of data movement and transformation remains an architectural decision even when an LLM helps author part of the workflow.
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Data engineering for LLM and RAG applications
Data integration also prepares context for retrieval and model workflows. Google describes unified, high-quality data as a foundation for grounding generative AI. AWS’s generative-AI data lifecycle guidance covers preparing data, integrating it into retrieval or fine-tuning workflows, collecting feedback, and updating data. Example preparation tasks include deduplication and removing sensitive personal information.
Those steps make familiar data-engineering responsibilities especially visible: quality checks, privacy and security controls, lineage, versioning, scalability, and cost management. AWS’s Generative AI Lens identifies these as architecture considerations. An LLM-assisted pipeline still needs controls that let a team understand what data was used, how it changed, and who or what can access it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means for data teams
Use generative AI where it can help initiate or explain a bounded task, such as drafting a PySpark transformation or suggesting a troubleshooting path. Keep responsibility for requirements, data permissions, validation, and deployment with the engineering process. The practical change is an additional way to work with pipeline tools—not a reason to skip sound ETL or ELT design and operational review.
Sources: AWS Glue and Amazon Q documentation; Google Cloud Data Engineering Agent API; Google Cloud data foundation guidance; BigQuery ETL and ELT guidance; Microsoft Learn RAG solution design guidance; AWS generative-AI data preparation guidance; AWS Glue.
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