ules was not justified, Congress voted to repeal those rules in March 2017. 8.2.4 THE HADOOP ECOSYSTEM The Hadoop ecosystem emerged primarily to deal with the large volumes of data, and ultimately with many aspects of the 7 Vs. Hadoop's originations come from Google, who in October 2003 published the "Google file system" article [Goog03], which outlined how to store large amounts of data cost effectively using common off-the-shelf hardware. This article was subsequently followed by another Google research article entitled "MapReduce: Simplified data processing on large clus- ters," which outlined how to process large amounts of data in a parallel fashion using many servers. From these background articles, the open source Apache Nutch project was born, which ultimately was named "Hadoop" by Doug Cutting who was working at Yahoo at the time [WikiHadoop]. Since then, the Hadoop ecosystem has evolved into many open source components covering the Hadoop distributed file system (HDFS), data ingestion, resource management, data processing approaches for analytics, batch, real time, search, ML, database storage approaches, and coordination and workflow management approaches as shown in Figure 8.3. Today, the Apache software foundation [Apac] manages the updates to the Hadoop ecosystem components. There are several popular supported distributions of the Hadoop ecosystem from Analytics Batch Stream/ Search Machine NoSQL (Not Only SQL) Real-time Learning Storage Key-value stores SPARK Reduce SPARK SPARK (Voldemort, Redis) Document stores STORM MAHOUT (CouchDB, MongoDB) Column-oriented MLlib (Hbase,Cassandra) Graph based (Titan, Neo4j, OrientDB) Cluster Resrouce Managemnt Data Ingestion Distributed file system Kafka, Flume, Scoop Data Sources FIGURE 8.3 The Hadoop ecosystem and its components. Building the Network of the Future Hortonworks [Hort], Cloudera [Clou], and Map-R [Mapr]. The following subsections outline each of these components, and their importance. 8.2.4.1 Storage: The HDFS The HDFS was a breakthrough in 2003, 