Databricks and PySpark | Full Course | - AI Agents for Data Engineers: Beyond Chatbots and Into Automation | @thedatamaster

Course links to enroll in Data AI Courses: 1. Gen AI Live Training: https://www.geekcoders.co.in/courses/Generative-AI-Engineering-Build-Deploy-and-Scale-684ae51d68b9ff35a8d2a044 2. Databricks Gen Ai Application :https://www.geekcoders.co.in/courses/Build-AI-Application-using-Databricks-and-LLM-67b23cff7347fd582a59a92d 3. Databricks Course: https://www.geekcoders.co.in/courses/Databricks-Certified-Data-Engineer-Zero-to-Hero-65eea8c0940e6c57bba246f7 4. PySpark Course: https://www.geekcoders.co.in/courses/Practice-50-PySpark-Interview-Questions-65f317e33210a77d47c6afaf 🎬 Intro [0:00 – 1:30] Sagar: Hey friends! Welcome to GeekCoders, and today we have a very special guest — Naval Yemul, the founder of The Data Master and one of the top voices in Databricks, Fabric, and Generative AI. Today, we’re going deep into a hot topic: AI Agents — not just for ChatGPT-style conversations, but for real, practical uses in data engineering. Welcome Naval! Naval: Thank you, Sagar. Super excited to be here. And even more excited that we’re doing this podcast together! 🧠 Segment 1: What Are AI Agents? [1:30 – 6:00] Sagar: So Naval, let’s start simple — what exactly is an AI Agent? How is it different from a chatbot? Naval: Great question. An AI Agent is a system that doesn’t just give answers — it can take actions. It understands your intent, uses tools like APIs or databases, makes decisions, and sometimes even maintains memory. Chatbots are passive. Agents are active — they can retrieve data, write SQL, access systems, and complete tasks using reasoning. 🛠️ Segment 2: What Tools and Frameworks Power These Agents? [6:00 – 11:00] Sagar: Okay, so what are some popular tools or frameworks people use to build these agents? Naval: The ecosystem is growing fast. But the major ones are: LangChain – The most popular framework for building agent workflows. AutoGen by Microsoft – Great for multi-agent collaboration. Databricks GenAI & MLflow agents – For enterprise-grade deployments inside your lakehouse. Haystack and RAG pipelines for Retrieval-Augmented Generation. We also use embedding models and vector databases like FAISS, Weaviate, or even Databricks’ built-in vector support. 💡 Segment 3: What Are Some Real-World Use Cases for Data Engineers? [11:00 – 18:00] Sagar: This sounds amazing, but let’s bring it home — what are some practical use cases for AI Agents in data engineering? Naval: Tons! Here are my top 5: SQL Copilot – Agent that writes and explains SQL queries for your lakehouse data. Schema Agent – Answers questions about table structure, joins, relationships. Pipeline Debugger Agent – Suggests fixes for failed ETL jobs based on logs. Governance Advisor – Recommends row/column masking based on PII detection. Documentation Assistant – Automatically generates docs for data pipelines or dashboards. These agents save hours of time, and they act like intelligent assistants for every data team. 👨‍💻 Segment 4: Example From Your Work – What Have You Built? [18:00 – 23:00] Sagar: Have you built any of these yourself? Could you share one recent example? Naval: Yes! I recently built a LangChain agent on Databricks. It uses: Unity Catalog to fetch schema and metadata Embeddings for natural language question answering RAG to retrieve relevant context OpenAI or DBRX to answer or even write SQL MLflow and Unity Catalog for versioned deployment So for example, the agent answers: "How many transactions were there in Q1?" …and it writes a complete SQL query and runs it. 🧱 Segment 5: How Is This All Structured? What’s Under the Hood? [23:00 – 28:00] Sagar: And behind the scenes — what are the key building blocks of such an agent? Naval: It usually has 5 core components: LLM – Like OpenAI, LLaMA, or DBRX Prompt Engineering – You define how the agent behaves Memory – So it can retain context of the conversation Tools – Like SQL runner, REST APIs, data lookup Orchestration Framework – LangChain, AutoGen, or custom logic And of course, you wrap it all inside a Python app or deploy it via Databricks notebooks, APIs, or Streamlit. 🔮 Segment 6: What’s the Future of AI Agents in Data Engineering? [28:00 – 32:00] Sagar: Where is this heading? Do you think agents will replace data engineers? Naval: Not replace — but they’ll transform how we work. Data engineers will move from writing repetitive code to designing intelligent workflows that include agents. We’ll see: Agents monitoring data quality Agents fixing pipeline issues before we even notice them Personal copilots for every data engineer So the future is not about being replaced… it's about who can use AI better. #aiagents #llm #AI