Japan Visa Analysis: Azure Data End to End Data Engineering

In this tutorial, you will set up the Spark master-worker architecture in a Docker container on Azure. 🚀 We'll then perform end-to-end data processing and visualization of visa numbers in Japan using PySpark and Plotly. 📈 Learn how to clean, transform, and visualize your data in an interactive manner, and gain insights into visa trends in Japan. 🇯🇵

What You Will Learn:
🛠 Setting up Spark master-worker architecture in Docker on Azure.
📖 Reading and cleaning data using PySpark.
🔄 Data transformation techniques with PySpark.
🎨 Visualizing data trends using Plotly Express.
💾 Exporting your visualizations and cleaned data.

Timestamps:
0:00 Introduction
1:15 Setting up the system architecture
05:00 Setting up cloud clusters
17:05 Coding
55:00 Results

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Resources and Links:
Github Code: https://github.com/airscholar/Japan-visa-data-engineering.git
Dataset: https://www.kaggle.com/datasets/yutodennou/visa-issuance-by-nationality-and-region-in-japan
Docker Documentation: https://docs.docker.com/engine/install/ubuntu/
Spark Official Documentation: https://spark.apache.org/docs/latest/api/python/index.html
Pyspark Documentation: https://pypi.org/project/pyspark/
Python Levenshtein Documentation: https://pypi.org/project/python-Levenshtein/

Tags:
PySpark, Plotly, Data Visualization, Data Cleaning, Docker, Azure, Spark Architecture, Data Analysis

Hashtags:
#PySpark #Plotly #DataVisualization #Azure #Docker #SparkTutorial #DataAnalysis