By Suman Paul

Spark

Apache Spark is an open-source big data processing framework used for handling large-scale data analytics and machine learning.

What Spark Is

• Spark is a data processing engine (like a super-fast calculator for huge amounts of data).
• It was designed to be faster and easier than older tools like Hadoop MapReduce.
• It works in clusters (multiple computers working together), so it can process massive datasets that don’t fit on one machine.

Hadoop, through MapReduce, processes data by reading and writing to disk at each step, which makes it slower but reliable for large-scale batch processing.

Core Concepts

1. Spark Architecture

2. DAG (Directed Acyclic Graph)

3. Lazy Evaluation

4. RDD vs Dataframes

Pyspark

PySpark is the Python API for Apache Spark. Apache Spark is a fast, distributed computing system for large-scale data processing. PySpark allows you to write Spark applications using Python instead of Scala or Java.

Key Advantages:

• Handles large datasets that don’t fit in memory.
• Distributed processing across clusters.
• In-memory computation for speed.
• Supports SQL queries, streaming, machine learning, and graph processing.