Medallion Architecture in Data Engineering

Medallion Architecture is a data design pattern used to organize data in a lakehouse, aiming to incrementally improve the quality of data as it flows through various layers. This architecture is often employed in big data systems to handle large volumes of data efficiently and effectively.

Key Layers of Medallion Architecture:

1. Bronze Layer (Raw Data):

   • Stores raw, unprocessed data.

   • Example: Logs, sensor data, or any other raw data ingested from various sources.

2. Silver Layer (Cleansed Data):

   • Contains data that has been cleaned and transformed.

   • Example: Data with duplicates removed, standardized formats, and basic transformations applied.

3. Gold Layer (Enriched Data):

   • Holds enriched and aggregated data ready for analysis.

   • Example: Data that has been aggregated, enriched with additional context, and is ready for reporting or machine learning.

Implementing Medallion Architecture with AWS S3, PySpark, and Jupyter Notebook

Step 1: Data Ingestion into AWS S3 (Bronze Layer)

• Store raw data in S3 buckets.

• Example Python code to upload data to S3:Python

  import boto3
  
  s3 = boto3.client('s3')
  s3.upload_file('local_file.csv', 'your-bucket-name', 'bronze/local_file.csv')

Step 2: Data Cleansing with PySpark (Silver Layer)

• Transform raw data using PySpark and store the cleansed data back in S3.

• Example PySpark code in a Jupyter Notebook:Python

  from pyspark.sql import SparkSession
  
  spark = SparkSession.builder.appName('MedallionArchitecture').getOrCreate()
  
  # Read raw data from S3
  df = spark.read.csv('s3a://your-bucket-name/bronze/local_file.csv', header=True, inferSchema=True)
  
  # Perform data cleansing
  df_cleaned = df.dropDuplicates().filter(df['column_name'].isNotNull())
  
  # Write cleansed data to S3
  df_cleaned.write.csv('s3a://your-bucket-name/silver/cleaned_file.csv', header=True)

Step 3: Data Enrichment and Aggregation (Gold Layer)

• Enrich and aggregate data using PySpark and store the final dataset in S3.

• Example PySpark code in a Jupyter Notebook:Python

  # Read cleansed data from S3
  df_cleaned = spark.read.csv('s3a://your-bucket-name/silver/cleaned_file.csv', header=True, inferSchema=True)
  
  # Perform data enrichment and aggregation
  df_enriched = df_cleaned.groupBy('group_column').agg({'value_column': 'sum'})
  
  # Write enriched data to S3
  df_enriched.write.csv('s3a://your-bucket-name/gold/enriched_file.csv', header=True)

Visualization and Analysis

• Visualize and analyze data using tools like Jupyter Notebook.

• Example code to visualize data:Python

  import pandas as pd
  import matplotlib.pyplot as plt
  
  # Read enriched data from S3
  df_enriched = pd.read_csv('s3://your-bucket-name/gold/enriched_file.csv')
  
  # Plot data
  df_enriched.plot(kind='bar', x='group_column', y='sum(value_column)')
  plt.show()

By following these steps, you can implement the Medallion Architecture using AWS S3 for storage, PySpark for data processing, and Jupyter Notebook for development and visualization.