I. Overview:
Apache Spark is a powerful open-source distributed computing system that provides fast and general-purpose cluster computing for big data processing. PySpark is the Python API for Spark, allowing you to use Spark’s capabilities with Python.
1. Key Features:
- Distributed Computing: Enables processing large datasets in parallel across a cluster.
- Ease of Use: Provides a Pythonic interface for working with big data, making it accessible to Python developers.
- Versatility: Supports various data formats, libraries, and APIs for diverse big data processing tasks.
2. Components of PySpark:
- Spark Core: The foundation of Apache Spark, providing distributed task dispatching, scheduling, and basic I/O functionalities.
- Spark SQL: Allows SQL queries on structured data using DataFrames and Datasets.
- Spark Streaming: Enables processing real-time streaming data.
- MLlib: A scalable machine learning library for Spark.
- GraphX: Graph processing library for analyzing graph-structured data.
