Data Engineering

Understand the three core Spark data abstractions, how they differ, why DataFrames became the preferred choice for most workloads, and when RDDs or Datasets still make sense. If you’ve started learning Apache Spark, you’ve probably encountered three terms repeatedly: RDD. DataFrame. Dataset. At first, they can seem interchangeable. After all, all three allow you to … Read more

Understand what SparkSession is, why modern Spark applications need it, what it actually creates, and how it differs from SparkContext If you’ve written PySpark code, you’ve probably started with something like this: And then immediately started working with DataFrames: But what exactly is spark? Why do we need a SparkSession? What happens when getOrCreate() is … Read more

Learn how Spark reads data from files and storage systems, how schema inference works, and how to build reliable data ingestion pipelines with PySpark Almost every Spark application starts with one simple question: Where is the data, and how do I get it into Spark? In PySpark, the answer usually starts with: For example: or: … Read more

Apache Spark makes distributed data processing look deceptively simple. You write something like: It looks like a few lines of Python. But Spark has to answer several questions before this computation can actually run: This is where jobs, stages, and tasks become important. In the previous tutorial, we discussed how Spark builds a DAG (Directed … Read more

Understanding how Spark moves data across partitions — and why shuffle is one of the biggest causes of slow Spark jobs You write a simple Spark query: It looks harmless. But behind the scenes, Spark may need to move millions or billions of records across the cluster so that all records belonging to the same … Read more

Understanding Directed Acyclic Graphs, transformations, actions, dependencies, stages, shuffles, and how Spark turns your PySpark code into distributed execution. You write a few lines of PySpark: It looks like a simple sequence of operations. But Spark doesn’t simply execute these lines one after another. Instead, Spark builds a representation of the computation, analyzes the dependencies … Read more

Understanding transformations, actions, execution plans, and why Spark waits before processing your data. If you have worked with PySpark, you may have noticed something unusual. You can write several transformations: And Spark may appear to do nothing. No computation.No immediate scan of the entire dataset.No obvious execution. But the moment you run: or: Spark suddenly … Read more

Learn what Apache Spark is, why it has become the standard for big data processing, how it works at a high level, and where it fits into today’s data engineering ecosystem. Introduction If you’ve started exploring data engineering, you’ve probably noticed one technology appearing everywhere—Apache Spark. Whether you’re working with Databricks, Delta Lake, Azure Data … Read more

Data Engineering interviews at mid-to-senior levels focus on complex real-world problems beyond basic SQL and ETL. Key topics include performance bottlenecks, data skew, and using SQL window functions like ROW_NUMBER(), RANK(), and DENSE_RANK(). Candidates must demonstrate their understanding of optimizing Spark jobs and partitioning strategies in data management.

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.