Tejas Agrawal

In today’s data-driven world, Pandas has become an essential tool for every Data Engineer. Whether you're building ETL pipelines, transforming large datasets, or preparing data for analytics and machine learning, Pandas plays a critical role in delivering clean, structured, and high-quality data 1. What makes Pandas different from NumPy? Answer: Pandas is built on top of NumPy but provides labeled , tabular , and time-series  data structures like DataFrame and Series.It supports: Missing...

Data engineers and analysts often rely on NumPy for efficient numerical computing and data manipulation. Mastering key NumPy concepts can give you an edge in interviews and help you handle real-world data challenges. This post presents 10 essential NumPy interview questions with clear, professional answers designed to prepare you for technical discussions and practical tasks. 1. Why is NumPy faster than Python lists? Answer: NumPy is faster because it stores data in contiguous memory blocks...

Medallion Architecture Question 1: Handling "SCD Type 2" for Patient Demographics Scenario :  Patient data, such as addresses and insurance details, changes over time. We need to maintain a full history of these changes for audit . purposes . Question:  How would you design the schema and ETL process to handle Slowly Changing Dimensions (SCD) Type 2 for a patient dimension table? Answer: Schema Design:  I would add three columns to the patient dimension table: start_date, end_date, and...