This blog is based on actual questions asked in a TCS Data Engineer Interview (Round 1) and, more importantly, explains what interviewers really expect behind each question. If you are preparing for TCS or similar data engineering roles, this guide will help you align your answers with practical thinking rather than theoretical explanations.
Tag: Big data
25 Most Commonly Asked AWS Redshift Interview Questions (Big Data)
Amazon Redshift is one of the most popular cloud data warehouses used in modern big-data architectures. Whether you are building ELT pipelines, performing analytics at scale, or optimizing workloads, Redshift is a crucial skill for Data Engineers.
In this article, I’ve compiled the 25 most frequently asked AWS Redshift interview questions, along with answers.
Databricks | Building an ETL Pipeline on Road Accident Data Using PySpark
When I started learning data engineering, I always wanted to try a real-world dataset instead of just “toy” examples. So I picked up the India Road Accident Dataset from Kaggle and built a complete ETL pipeline using PySpark and Delta Lake.
Note: This project is a sample ETL pipeline I built for learning and practice. It’s not production-ready, but it’s a great way to understand how raw data becomes analytics-ready data step by step.
In this blog, I’ll walk you through how I designed the pipeline using the Medallion Architecture (Bronze → Silver → Gold). Don’t worry if the terms sound heavy, I’ll explain everything in plain English
How to Become a Data Engineer from a Non-Technical Background: A Step-by-Step Guide
Are you interested in transitioning into data engineering, even though your background is not in technology? You’re not alone. Many people from fields like business, healthcare, or the arts dream of harnessing the power of data but worry that their lack of technical experience will hold them back. The good news: breaking into data engineering is absolutely possible—with a roadmap and determination.
PySpark | How to Sort a Dataframe?
Sorting data is a fundamental task in data processing, whether for analysis, reporting, or data transformation. In PySpark, sorting a DataFrame is a common operation that allows you to organize your data based on one or more columns. PySpark provides multiple ways to sort data efficiently, even when dealing with large datasets distributed across clusters.
In this blog post, we’ll explore various methods to sort a DataFrame in PySpark, covering both ascending and descending orders, sorting by multiple columns, and handling null values during sorting.
PySpark | How to Filter Data in DataFrame?
Filtering data is one of the most common operations you’ll perform when working with PySpark DataFrames. Whether you’re analyzing large datasets, preparing data for machine learning models, or performing transformations, you often need to isolate specific subsets of data based on certain conditions. PySpark provides several methods for filtering DataFrames, and this article will explore the most widely used approaches.
PySpark | How to Add a New Column in a Dataframe?
In PySpark, adding a new column to a DataFrame is a common and essential operation, often used for transforming data, performing calculations, or enriching the dataset. PySpark offers 3 main methods for this: withColumn(),select() and selectExpr(). These methods allow you to create new columns, but they serve different purposes and are used in different contexts.
This article will guide you through adding new columns using both methods, explaining their use cases and providing examples.
PySpark | How to Create a Dataframe?
In PySpark, a DataFrame is a distributed collection of data organized into named columns, similar to a table in a relational database or an Excel spreadsheet. DataFrames provide a powerful abstraction for working with structured data, offering ease of use, high-level transformations, and optimization features like catalyst and Tungsten. This article will cover how to […]
PySpark | How to Create a RDD?
Resilient Distributed Datasets (RDDs) are the core abstraction in PySpark, offering fault-tolerant, distributed data structures that can be operated on in parallel. Although the DataFrame API is more popular due to its higher-level abstractions, RDDs are still fundamental for certain low-level operations and are the building blocks of PySpark.
In this article, you’ll learn how to create RDDs in PySpark, the different ways to create them, and when you should use RDDs over DataFrames.
PySpark | How to Create a Spark Session?
Creating a Spark session is the first step when working with PySpark, as it allows you to interact with Spark’s core functionality. This article will walk you through the process of creating a Spark session in PySpark.