Generative AI: Elevate your Data Engineering Career

IBM examines how generative AI can support tasks within a data-engineering workflow. The examples include data generation, augmentation and infrastructure or schema-related work. It is an applied continuation that still requires learners to evaluate generated output against the needs of the data system.

What you will learn

• Compare generative AI uses in data engineering.
• Evaluate generated and augmented data.
• Apply AI-assisted ideas to schema and infrastructure tasks.

Who this suits

Learners with foundations in generative AI, prompt engineering and data engineering, following the stated prerequisites.

Before you enroll

Check the software versions, cloud-account requirements and lab access terms before enrolling. Running cloud resources can incur separate charges. Use fictional or public sample data, and evaluate reliability, permissions and data quality alongside whether a pipeline runs.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Generate a fictional test dataset and review it for missing cases, invalid relationships and assumptions before using it in a pipeline test.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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