"From Zero to Data Engineer: Build Your First Data Pipeline with Python, Airflow, DBT & BigQuery"

This session walks through the complete journey of building a production-ready data pipeline from scratch. Whether you're new to data engineering or looking to strengthen your fundamentals, this hands-on walkthrough with Airflow, dbt, BigQuery, and Docker shows how modern data teams build scalable, reliable pipelines end to end.

📘 What You Will Learn

🔹What a real-world data pipeline looks like and how each component works
🔹How to extract, load, transform, and analyze weather data using industry best practices
🔹Setting up orchestrations in Apache Airflow, including DAGs, scheduling, retries, and monitoring
🔹Loading and optimizing data in BigQuery (partitioning, clustering, schema inference)
🔹Building modular SQL transformations using dbt across staging, intermediate, and mart layers
🔹Deploying the entire workflow using Docker with a single command
🔹Best practices in separation of concerns, data quality, observability, and scalability

🧩 Topics Covered

🔹Understanding data pipelines and architecture fundamentals
🔹End-to-end weather data pipeline overview (Extract → Load → Transform → Analyze)
🔹Python extraction components (API extractor, CSV extractor, validation, metadata)
🔹Airflow workflow orchestration and real-time monitoring
🔹BigQuery loading strategies and performance optimization
🔹dbt models, testing, documentation, and lineage graphs
🔹Docker-based deployment for reproducible environments
🔹Demonstrated best practices: modularity, validation, logging, data quality checks, scalability
🔹Live demo and walkthrough
🔹Q&A