Learn AI Data Engineering in a Month of Lunches

Data engineering often means wrestling with repetitive code, untidy inputs, and pipelines that need constant attention. Learn AI Data Engineering in a Month of Lunches by David Melillo looks at how AI can help with that work—using SQL, Python, APIs, and practical data problems rather than stopping at conversational prompts.

Across short, hands-on chapters, the book shows ways to bring large language models into everyday workflows while keeping people responsible for evaluating results and making decisions. Its focus is practical: use AI to reduce friction, improve data handling, and give engineers more room to concentrate on the problems behind the data.

From coding companion to working data pipeline

The opening section examines AI-assisted work with SQL and Python, including coding companions and the OpenAI API. From there, the material turns to data quality, cleaning, transformations, and the broader data lifecycle. Readers encounter applications such as extracting and structuring information from unstructured sources, working with scraped data, and building AI-assisted pipeline steps.

Practical themes across the chapters

• AI in SQL and Python: Explore coding assistance, prompts, APIs, and programmatic applications in data work.
• Cleaning and transforming data: Consider how AI can help identify quality issues and handle complex or inconsistent inputs.
• Data generation and enrichment: Work through web scraping and ways to use AI with unstructured information.
• Agentic workflows: Explore approaches that apply AI to decision paths and data activation in engineering contexts.

Built around practice, not just prompts

The book’s “Month of Lunches” approach breaks the subject into manageable lessons, supported by labs. The appendices include material on setting up an environment, prompt engineering, using the OpenAI API, datasets, and troubleshooting—useful companions to the main chapters as readers work through the examples.

Who may find it useful

Data engineers are the central audience, particularly those looking to apply AI beyond an IDE assistant or a chat window. Analysts, data scientists, and other data practitioners interested in using AI for ingestion, transformation, or enrichment may also find relevant ideas here. Some familiarity with SQL and Python can help readers get started.

Melillo’s core emphasis is a practical one: treat AI as a tool for reducing repetitive effort, while bringing human judgment to the work that still needs it.