1. Spark Core: Spark Core is the underlying general execution engine for spark platform that all other functionality is built upon. It provides in-memory computing capabilities to deliver speed, a general execution model to support a wide variety of applications, and Java, Scala, and Python APIs for ease of development.
2. Spark SQL: Spark SQL is the component of Spark which provides a programming abstraction called DataFrames and can also act as distributed SQL query engine. It allows developers to intermix SQL queries with the programmatic data manipulations supported by RDDs in Python, Java, and Scala.
3. Spark Streaming: Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Twitter, etc.
4. MLlib: MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, and underlying optimization primitives.
5. GraphX: GraphX is the Spark API for graphs and graph-parallel computation. It provides a set of fundamental operators for manipulating graphs and a library of common algorithms. It also provides various utilities for indexing and partitioning graphs and for generating random and structured graphs.