In modern data engineering, handling continuously arriving data efficiently is one of the biggest challenges. Traditional batch processing methods often struggle when new files arrive frequently, ...

When to Use: Use for Blob/ADLS files when a sample is available ️ Option: None Meaning: No schema imported (schema-less) When to Use: Use for dynamic or parameterized pipelines 💡 Best Practice For ...

This project demonstrates data wrangling and analysis using PySpark in Azure Databricks, focusing on cleaning and transforming a mock dataset from an electrical meter reading system. It also showcases ...

🚀 Instacart Medallion Data Engineering Pipeline using PySpark & Airflow 📌 Project Overview Built an end-to-end Data Engineering pipeline processing over 32.4 million order-product records and 3.4 ...