Database-Technology Comparison Matrix, October 14, http://accionlabs.com/ choosing-a-nosql-database-technology-comparison-matrix/. Building the Network of the Future Statistics for monitoring Linearly scalable Configurable, XML configuration Often after utilizing any of the various batch/real-time/analytic Hadoop ecosystem functions to process/crunch the data. SOLR is used to index it and serve it up for consumption. SOLR can also be used as a key-value store via its support for range queries. 8.2.4.6 Batch Processing (MapReduce, HIVE, PIG) As mentioned previously, Hadoop originated with batch processing of large datasets with MapReduce. MapReduce allows parallel processing of data by performing a "divide and conquer" technique by dividing the data up across multiple servers in the map phase into key/value pairs and then after merging, combining, and sorting the data it uses a reduce phase to processes the data in parallel. To illustrate how MapReduce works, consider the illustration in Figure 8.4, which performs the simple task of "how to count the number of times a given word is used in a document." The data from a document are first divided into two documents across two servers and words are separated and individually tallied. They are then combined and sorted, after which the tallies are summed in the reduce phase. MapReduce, however, has somewhat fallen out of favor in the ML community given its high overhead costs, and YARN has allowed alternate processing approaches like SPARK to exist in conjunction with MapReduce. To overcome some of the challenges of breaking down a computa- tion task into map and reduce function, many programmers utilize the programming language PIG Latin, which is normally simply referred to as PIG. PIG supports user-defined functions written in Java, Python, etc. and then translates into MapReduce jobs. HIVE is another popular batch processing approach, which allows querying and summarizing of data stored in HDFS and NoSQL databases using HIVEQL, which is like MySQL. 8.2.4.7 Real-Tim