Spark

Building an ETL Pipeline with Spark

A practical guide to designing extract, transform, and load stages in Spark, from schema-safe ingestion to atomic loads.

DataFrames Basics

How Spark's DataFrame API represents distributed, schema-aware tabular data and why it outperforms hand-written RDD code.

Installing and Running Spark

How to install Apache Spark's prerequisites, run it locally for development, and choose the right cluster mode for production.

Joins in Spark

How Catalyst chooses between broadcast hash joins and sort-merge joins, why the choice matters for performance, and how to diagnose and fix skewed joins.

Lazy Evaluation and the DAG

How Spark defers execution of transformations into a logical plan and only runs work when an action forces it, using a DAG of stages for both optimization and…

Monitoring with the Spark UI

Navigate the Spark web UI to diagnose job performance, stage bottlenecks, and executor behavior in running and completed applications.

Partitioning and Shuffling

How Spark distributes data into partitions for parallelism, why shuffles are the costliest operation in a job, and how to control partition count and skew.

Spark Architecture

How Spark's driver, cluster manager, and executors work together to run a distributed job, from DAG construction to parallel task execution.

Spark Best Practices

Practical guidelines for writing efficient, reliable, and maintainable Apache Spark jobs in production.

Spark Interview Questions

The core concepts, performance-tuning questions, and system-design scenarios that come up most often in Apache Spark interviews.

Spark Quick Reference

A condensed cheat sheet of Spark's core APIs, common transformations and actions, I/O options, and key configuration settings.

spark-submit and Deployment

Master the spark-submit command-line tool and the client vs. cluster deployment modes for running Spark applications.

Spark vs Hadoop MapReduce

How Spark's in-memory DAG execution model compares to classic Hadoop MapReduce in performance, fault tolerance, and use cases.

Spark with PySpark

Understand how PySpark bridges Python code to the JVM-based Spark engine, and how to write efficient PySpark DataFrame and UDF code.

The Spark SQL API

How Spark SQL lets you query DataFrames with standard SQL, compiling to the same Catalyst-optimized plan as the DataFrame API.

What Is Apache Spark?

An introduction to Apache Spark as a unified, in-memory distributed computing engine and why it replaced Hadoop MapReduce for many workloads.

Window Functions in Spark SQL

Understand how to compute running totals, rankings, and moving averages across partitions of rows using Spark's window function API.

Your First Spark Job

Writing a complete read-transform-write Spark job, and understanding the crucial difference between lazy transformations and eager actions.

Flink vs Spark Streaming

A technical comparison of Flink's true streaming model against Spark Structured Streaming's micro-batch engine.

Hadoop vs Spark

A technical comparison of MapReduce's disk-based batch model and Spark's in-memory DAG execution, and when to reach for each.

Scala and Apache Spark

Apache Spark's Scala API exposes distributed, fault-tolerant data processing through RDDs, DataFrames, and Datasets, letting Scala code scale from a laptop to…