The Spark ecosystem includes five key components:

1. Spark Core is a general-purpose, distributed data processing engine. On top of it sit libraries for SQL, stream processing, machine learning, and graph computation—all of which can be used together in an application. Spark Core is the base of a whole project, providing distributed task dispatching, scheduling, and basic I/O functionalities.

2. Spark SQL is the Spark module for working with structured data that supports a common way to access a variety of data sources. It lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Spark SQL supports the HiveQL syntax and allows access to existing Apache Hive warehouses. A server mode provides standard connectivity through Java database connectivity or open database connectivity.

3. Spark Streaming makes it easy to build scalable, fault-tolerant streaming solutions. It brings the Spark language-integrated API to stream processing, so you can write streaming jobs in the same way as batch jobs. Spark Streaming supports Java, Scala, and Python, and features stateful, exactly-once semantics out of the box. 

4. MLlib is the Spark scalable machine learning library with tools that make practical ML scalable and easy. MLlib contains many common learning algorithms, such as classification, regression, recommendation, and clustering. It also contains workflow and other utilities, including feature transformations, ML pipeline construction, model evaluation, distributed linear algebra, and statistics. 

5. GraphX is the Spark API for graphs and graph-parallel computation. It’s flexible and works seamlessly with both graphs and collections—unifying extract, transform, load; exploratory analysis; and iterative graph computation within one system. In addition to a highly flexible API, GraphX comes with a variety of graph algorithms. It competes on performance with the fastest graph systems, while retaining the flexibility, fault tolerance, and ease of use of Spark.