Building Self-Healing Data Pipeline - End to End Data Engineering Project

In this video, I'll show you how to build a production-ready, AI-powered data pipeline that automatically detects and heals data quality issues in real-time. No more failed pipelines because of bad data! We'll combine the power of Apache Airflow 3.0 with Ollama (running LLaMA 3.2 locally) to create an intelligent pipeline that: ✅ Automatically diagnoses data quality issues (missing values, wrong types, malformed text) ✅ Self-heals problematic records without manual intervention ✅ Performs sentiment analysis on millions of Yelp reviews using local LLM ✅ Generates comprehensive health reports and metrics ✅ Gracefully degrades when things go wrong This is the future of data engineering - pipelines that think for themselves and fix problems before they become failures. What You'll Learn: ✅ How to build agentic workflows in Apache Airflow ✅ Integrating local LLMs (Ollama) into your data pipelines ✅ Implementing self-healing patterns for data quality ✅ Batch processing strategies for large datasets ✅ Building health monitoring and observability into pipelines Like this video? Support us:    / @codewithyu   Timestamps: 0:00 Introduction 1:43 System Architecture and background 5:49 Setting up the project 13:27 The Agentic Self Healing Pipeline 17:00 Embedding AI Agents in Airflow 40:44 Diagnosing and Healing Pipelines 1:11:44 Generating Health Reports 1:16:12 Results and Review 1:30:00 Outro Resources: Read more: https://open.substack.com/pub/datainp... Full Code+Video: https://buymeacoffee.com/yusuf.ganiyu... Full Source Code: https://github.com/airscholar/SelfHea... Ollama Download: https://ollama.com/download Apache Airflow: https://airflow.apache.org/ Connect With Me: LinkedIn:   / yusuf-ganiyu   GitHub: https://github.com/airscholar Twitter/X: https://x.com/yusufOGaniyu #dataengineering #airflow #python #llm #ollama #datapipeline #machinelearning #ai #selfhealing #apacheairflow #dataengineer #etl #dataquality