How to implement a Medallion architecture

Steps to Implement Medallion Architecture:

Ingest Data into the Bronze Layer:

Load raw data from external sources (e.g., databases, APIs, file systems) into the Bronze layer.
Use Delta Lake to store raw data with minimal processing.

Example code to load data into Bronze layer:

bronze_df = spark.read.format("csv").option("header", "true").load("/path/to/raw/data")
bronze_df.write.format("delta").save("/path/to/bronze/table")
Transform Data into the Silver Layer:
Clean, validate, and conform the data from the Bronze layer.
Apply transformations such as filtering, deduplication, and data type corrections.
Store the transformed data in the Silver layer.

Example code to transform data into Silver layer:

bronze_df = spark.read.format("delta").load("/path/to/bronze/table")
silver_df = bronze_df.filter("column_name IS NOT NULL").dropDuplicates()
silver_df.write.format("delta").mode("overwrite").save("/path/to/silver/table")

Enrich Data into the Gold Layer:

Perform advanced transformations, aggregations, and enrichment on the Silver layer data.
Create highly refined datasets optimized for analytics and machine learning.
Store the enriched data in the Gold layer.

Example code to enrich data into Gold layer:

silver_df = spark.read.format("delta").load("/path/to/silver/table")
gold_df = silver_df.groupBy("category").agg({"value": "sum"})
gold_df.write.format("delta").mode("overwrite").save("/path/to/gold/table")

Example Workflow:
Ingest raw sales data into the Bronze layer.
Transform the raw data by removing duplicates and validating entries in the Silver layer.
Aggregate and analyze sales data by product category in the Gold layer for business intelligence.

Additional Tips:

Delta Live Tables (DLT): Use DLT to automate the creation and management of reliable data pipelines for the Medallion Architecture.
Scheduling: Use Databricks jobs to schedule regular updates to your Bronze, Silver, and Gold tables.
Monitoring: Monitor data quality and pipeline performance using built-in Databricks tools.