Data Engineering

Data engineering involves designing, constructing, and maintaining data architectures and systems. It focuses on transforming raw data into valuable information through processes such as ETL (Extract, Transform, Load).

Databricks Data Engineering Interview Questions — Part 2: Advanced Spark, Delta Lake & Production Scenarios

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In Part 1, we covered the fundamentals of Spark, PySpark, Databricks, DAGs, lazy evaluation, partitioning, data skew, salting, AQE, migration validation, and common PySpark coding questions. But experienced Data Engineer interviews usually go one level deeper. Interviewers want to know: Can you actually design, optimize, and troubleshoot a production data platform? In this article, we’ll cover … Read more

20 Data Engineering Interview Questions You Should Know for Databricks & PySpark Roles

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Data Engineering interviews now demand in-depth knowledge of Spark, PySpark, and Databricks beyond basic SQL and transformations. Candidates should understand concepts like Spark architecture, lazy evaluation, DAG, transformations, data skew, and troubleshooting techniques. Strong candidates demonstrate proficiency across coding, architecture, and production troubleshooting, distinguishing them in interviews.

Pyspark Tutorial for beginners

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S3 Bucket

PySpark, it is a Python library for Apache Spark, an open-source distributed computing system. PySpark allows you to write Spark applications using Python programming language, providing a Python API for Spark’s capabilities. Below are some properties of pyspark. 1. Create a simple Spark Dataframe 2. Read data from a table using spark.sql To read data … Read more

Extracting Financial Year from Date in Pandas and PySpark DataFrames

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Python

Introduction Working with date data often involves extracting relevant information, such as the financial year. In this blog post, we’ll explore how to extract the financial year from a date column in both Pandas and PySpark DataFrames. Extracting Financial Year in Pandas DataFrame Sample Data Let’s start by creating a sample Pandas DataFrame with a … Read more