This project provides a Docker-based environment for running Data Science workflows inside VS Code. Using Docker ensures reproducibility, isolation, and portability, so you can focus on coding without worrying about dependency issues.

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🔑 What is Docker?

👉 Docker is a platform that lets you package your applications (code, libraries, dependencies, OS) into a lightweight, portable unit called a container.

Think of Docker as a way to “ship your code with everything it needs” so it runs the same on any machine.

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🧩 Key Concepts

1. Docker Image 🖼️

• A blueprint (template) for containers.

• Defines the OS, libraries, tools, and environment your code needs.

• Example: A Python image with NumPy, Pandas, and Jupyter installed.

2. Docker Container 📦

• A running instance of an image.

• Think of it as a lightweight virtual machine that runs your project.

• You can start, stop, or remove containers anytime.

3. Docker Compose 🛠️

• A tool to manage multiple containers together using one YAML file (docker-compose.yml).

• Example:

  • One container runs Jupyter Notebook

  • Another runs a PostgreSQL database

  • Another runs Redis (cache)

• With Docker Compose, you can start all services with just:

    docker compose up
  

👉 For Data Science, Docker Compose is important because many projects need multiple services (e.g., Jupyter + Database + ML Model API).

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⚡ Why Use Docker for Data Science?

• ✅ Reproducibility → Same Python + library versions everywhere.

• ✅ Portability → Move your project from laptop → server → cloud easily.

• ✅ Isolation → Keep data science projects separated (no package conflicts).

• ✅ Scalability → Connect multiple tools (Jupyter, PostgreSQL, Spark, Hadoop, etc.) with Compose.

• ✅ Team Collaboration → Everyone gets the same environment.

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🧑‍💻 Usage for Data Engineers & Data Scientists

• Data Scientist → Run Jupyter, Python, ML libraries inside Docker.

• Data Engineer → Orchestrate multiple tools (Spark, Kafka, Hadoop, PostgreSQL) using Docker Compose.

• ML Engineer → Deploy trained ML models as APIs inside containers.

• Big Data Projects → Use Compose to manage clusters (HDFS + Spark + Airflow).

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🚀 Final Takeaway

• Docker → Packages your environment

• Image → Blueprint of environment

• Container → Running environment

• Docker Compose → Manages multiple services easily

👉 For Data Science & Engineering, Docker ensures smooth collaboration, reproducibility, and scalability.

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Redis

What is Redis? An in-memory key-value data store. Extremely fast, supports strings, lists, sets, hashes, sorted sets, pub/sub, TTLs, atomic ops, and simple persistence options.

Why use Redis?

• Super fast (in RAM) → ideal for caching and counters.

• Flexible data types → beyond plain key/value.

• Great for sessions, rate limiting, leaderboards, lightweight queues.

• Simple pub/sub messaging.

Common usages

• Cache DB or API responses.

• Session store for web apps.

• Rate limiting and counters.

• Message queue / pub-sub for background tasks.

• Leaderboards with sorted sets.

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Flask

What is Flask? A lightweight Python web framework (microframework). Gives routing, request handling, templates (Jinja2) and is easy to extend.

Why use Flask?

• Simple to learn and fast to prototype.

• Perfect for REST APIs (e.g., serve ML models).

• Highly customizable — pick only what you need.

• Works well with Docker and microservice architectures.

Common usages

• REST API for ML models.

• Small web apps or admin dashboards.

• Backend for SPA (single-page applications).

• Microservice endpoints in data pipelines.

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🔗 Why Use Redis + Flask in Docker?

1. Flask in Docker

• Flask is your web layer — the place where you expose APIs (for ML predictions, dashboards, or data services).

• In Docker, you package Flask with its exact Python version + dependencies → no “works on my machine” issues.

• Your Flask API becomes portable: run locally, on a server, or in the cloud with the same behavior.

Example use case: You trained an ML model → you wrap it in Flask (/predict endpoint) → Docker makes it a microservice → deploy anywhere.

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2. Redis in Docker

• Redis is your fast in-memory store (cache, session store, counters, pub/sub).

• In Docker, Redis runs as a separate isolated service — no need to install manually on your system.

• You can spin up Redis instantly for dev/test/prod using docker run or docker compose.

Example use case:

• Cache expensive ML predictions or database queries.

• Store Flask user sessions in Redis (instead of local memory).

• Count visits to your Flask app with Redis.

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✅ Benefits of Using Docker Here

• Isolation → Flask container has Python + deps, Redis container has its own environment. No conflicts.

• Reproducibility → Same versions across dev, staging, production.

• Portability → Run the same app locally, on server, or in cloud.

• Scalability → With Compose/Kubernetes you can scale Flask (docker compose up --scale web=3) while keeping Redis shared.

• Simplicity → One command (docker compose up) starts your whole stack.

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👉 In short:

• Flask = your web service (APIs, dashboards, ML endpoints)

• Redis = your cache/store (fast data access, counters, sessions)

• Docker = your packaging and orchestration (easy to run anywhere, together).

• 🐳 DOCKER COMPOSE

• 

Docker Compose is a tool that helps you define and manage multi-container Docker applications easily.

Instead of running containers one by one with long docker run commands, you can describe your entire setup in a single docker-compose.yml file and start everything with a single command.

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🔑 Key Points:

• Multi-container apps → Example: A Flask app + a Redis database.

• YAML configuration → You define services (containers), networks, and volumes in docker-compose.yml.

• One command → docker compose up will start all services together.

• Easy management → Stop everything with docker compose down.

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📝 Example (docker-compose.yml)

version: '3'
services:
  web:
    build: .
    ports:
      - "5000:5000"
  redis:
    image: "redis:alpine"

➡️ Here:

• web = Flask app (built from Dockerfile in the current directory .)

• redis = Redis container (from official image)

• Both run together with just:

docker compose up

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⚡ In short: Docker runs one container at a time, while Docker Compose helps run and connect multiple containers (like a whole project stack) with a single command.

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🐳 Docker Commands Cheat Sheet

🔨 Build Image

docker build -t welcome-app .

• -t welcome-app → names the image welcome-app

• . → build from current directory (important!)

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▶️ Run Container

docker run -d --name welcome-app -p 8000:5000 welcome-app

• -d → run in background (detach mode)

• --name welcome-app → names the container

• -p 8000:5000 → maps host port 8000 → container port 5000

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🗑️ Remove Image

docker rmi -f <image_id_or_name>

• -f → force remove

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📸 List Images

docker images

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📦 List Containers

docker ps -a

• Shows all containers (running + stopped).

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📑 Docker Compose

Start all services:

docker compose up

Build/rebuild images:

docker compose build

Stop services:

docker compose stop

Remove containers, networks, volumes (clean):

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🐳 Common Docker Commands

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❌ Common Errors

• Mistyping commands → Double-check command names (e.g., docker, not doker).

• Port already in use → Change port mapping (9000:5000 instead of 8000:5000).

• Module not found → Rebuild image with docker compose up --build.

• Redis connection error → Wait a few seconds (handled by retry loop).

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✅ With this, you have:

• A Flask app in Docker

• A Flask + Redis app in Docker Compose

📬 Let’s Connect: LinkedIn – Maddy Das

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