Hadoop was first released by Apache in 2011 as version 1.0.0. It only contained HDFS and MapReduce. Hadoop was designed as both a computing (MapReduce) and storage (HDFS) platform from the very beginning. With the increasing need for big data analysis, Hadoop attracts lots of other software to resolve big data questions together and merges to a Hadoop-centric big data ecosystem. The following diagram gives a brief introduction to the Hadoop ecosystem and the core software or components in the ecosystems:

The Hadoop ecosystem

In the current Hadoop ecosystem, HDFS is still the major storage option. On top of it, snappy, RCFile, Parquet, and ORCFile could be used for storage optimization. Core Hadoop MapReduce released a version 2.0 called Yarn for better performance and scalability. Spark and Tez as solutions for real-time processing are able to run on the Yarn to work with Hadoop closely. HBase is a leading NoSQL database, especially when there is a NoSQL database request on the deployed Hadoop clusters. Sqoop is still one of the leading and matured tools for exchanging data between Hadoop and relational databases. Flume is a matured distributed and reliable log-collecting tool to move or collect data to HDFS. Impala and Presto query directly against the data on HDFS for better performance. However, Hortonworks focuses on Stringer initiatives to make Hive 100 times faster. In addition, Hive over Spark and Hive over Tez offer a choice for users to run Hive on other computing frameworks rather than MapReduce. As a result, Hive is playing more important roles in the ecosystem than ever.