The Hadoop ecosystem is a framework and set of tools for processing large amounts of data. It consists of various components that work together to handle big data tasks. Here’s a brief overview of some of the main features:

1. Hadoop HDFS (Hadoop Distributed File System): This is the storage layer of Hadoop, which stores data across distributed clusters of servers.
2. Hadoop MapReduce: This processing layer enables data processing parallelly in a distributed form.
3. Hadoop YARN (Yet Another Resource Negotiator): A resource management layer manages and schedules resources across the cluster.
4. Hadoop Common: These are the standard utilities that support other Hadoop modules.
5. Pig: A platform for analyzing extensive data sets with a high-level language for expressing data analysis programs.
6. Hive: A data warehousing and SQL-like query language that presents data as tables.
7. HBase: A distributed and scalable database that supports structured data storage for large tables.
8. ZooKeeper: A centralized service for maintaining configuration information and providing distributed synchronization.
9. Sqoop: A tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases.
10. Flume: A service that collects, aggregates, and moves large amounts of log data.
11. Oozie: A workflow scheduler system to manage Apache Hadoop jobs.
12. Spark: An open-source, distributed computing system that can process data much faster than traditional Hadoop MapReduce.
13. Mahout: A distributed linear algebra framework and mathematically expressive Scala DSL.
14. Tez: An extensible framework for building high-performance batch and interactive data processing applications.

The Hadoop ecosystem is designed to scale up from a single server to thousands of machines, each offering local computation and storage. Organizations often use it to handle big data analytics and processing needs.