Common issues you will often encounter when running Airflow with Docker. ❗ Issue 1 — .env file is not visible inside Airflow container 🔍 Symptom Summary The .env file exists at the project root. But inside the Airflow container, load_dotenv() fails to read it. The reason: Docker automatically passes .env as environment variables. But Docker does not copy or mount the file itself into the container. Therefore, load_dotenv() has no file to read. ✅ Solution 1️⃣ Add volume mount for .env in docker-compose.yml This way, the .env file becomes available inside the container at the correct path. ...

If you start working with Airflow a bit more seriously, you’ll quickly notice that it’s usually split into multiple services: airflow-init airflow-scheduler airflow-webserver At first, you may wonder: “Why do we need to split them up like this?” Well — this is actually the standard production architecture. Let’s break it down in simple, practical terms. 1️⃣ airflow-init — Preparation Step Also sometimes called airflow-db-migrate or airflow-bootstrap. This runs only once when you initialize Airflow. ...

✨ Try It 👉 Launch the Potting Soil Calculator 📬 Source Code GitHub: https://github.com/namikimlab/potting-soil-calculator 🪴 Why I Built This When planting in pots, figuring out how much soil you need can be surprisingly tricky. The volume depends on the pot’s shape, size, height, and quantity — and beginner gardeners often don’t have a clear way to calculate it. So I decided to create a tool that allows users to quickly and intuitively calculate the soil volume needed for repotting. ...

Role dbt Does Well Python Does Better Structured data cleaning (staging) ✅ Possible, but inconvenient Designing mart table structures ✅ Also possible User-specific calculations ❌ Inconvenient ✅ Super flexible Scoring, conditional matching, if-else logic ❌ Very cumbersome ✅ Ideal Filtering based on user input ❌ Not possible ✅ Core feature Explaining recommendations, tuning logic ❌ ✅ Fully customizable For Example -- This kind of logic is painful in dbt... SELECT CASE WHEN user.age BETWEEN policy.min_age AND policy.max_age THEN 30 ELSE 0 END + CASE WHEN user.income < policy.income_ceiling THE_ ELSE 0 END + ... In dbt, the concept of a “user” doesn’t even exist dbt is built for models that apply the same logic to everyone Python, on the other hand, can generate different recommendations per user based on input 👉 dbt is great for static modeling, but dynamic, user-input-driven recommender systems are better suited for Python. ...

✨ Project Summary Assuming I am working for a fintech company, I built a batch pipeline that automatically aggregates → transforms → analyzes credit card data. Since I couldn’t use real data, I used synthetic transaction data generated using Faker, but I believe it was sufficient for the purpose of designing the overall data flow and structure. 🎯 Goal “Build an Airflow pipeline that processes realistic financial data with Spark, analyzes and stores them.” ...

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