Data Engineering with GenAI

Understand the role of Generative AI and LLMs in modern data engineering, including AI-assisted development, AI-ready data, use cases, limitations, and responsible AI.

Learn how to create reliable prompts for SQL, Python, transformation logic, documentation, debugging, testing, and data engineering workflows.

Use GenAI to accelerate SQL development, Python scripting, data modeling, query optimization, debugging, and technical documentation.

Apply Generative AI to data ingestion, transformation, schema mapping, pipeline development, testing, documentation, and troubleshooting.

Explore how Generative AI can support synthetic datasets, data augmentation, privacy protection, test data generation, and data-quality validation.

Learn how to process documents and unstructured information into clean, structured, enriched data suitable for GenAI and retrieval applications.

Understand embeddings, vector representations, similarity search, indexing, metadata filtering, and vector databases used in modern GenAI applications.

Build production-oriented RAG pipelines that connect AI-ready data, retrieval systems, vector search, and LLM applications.

Learn how LLMs and AI agents can interact with databases through schema-aware, validated, secure, and controlled data workflows.

Develop reliable AI-enabled data workflows using automated quality checks, anomaly detection, evaluation, monitoring, and troubleshooting practices.

Learn how to design secure, governed, privacy-aware data infrastructure for enterprise GenAI applications and production workflows.

Build an end-to-end GenAI data engineering solution connecting ingestion, transformation, AI-ready data, retrieval, LLM integration, evaluation, monitoring, and security.