![]() Mahout is yet another Apache project whose goal is to generate free applications of distributed and scalable machine learning algorithms that support big data analytics on the Hadoop platform. Versioning and version control are additional useful features. Its scope includes full text indexing and library search for use within a Java application.Īvro facilitates data serialization services. The Lucene project is used widely for text analytics/searches and has been incorporated into several open source projects. Oozie, an open source project, streamlines the workflow and coordination among the tasks. It also provides reliable service with no particular point of failure ( ) and it is a NoSQL system. It is designated as a top-level project modeled to handle big data distributed across many utility servers. It uses a non-SQL approach.Ĭassandra is also a distributed database system. HBase is a column-oriented database management system that sits on top of HDFS. ![]() Big data analytics applications utilize these services to coordinate parallel processing across big clusters. Zookeeper allows a centralized infrastructure with various services, providing synchronization across a cluster of servers. To facilitate parallel processing, Jaql converts “‘high-level’ queries into ‘low-level’ queries” consisting of MapReduce tasks. Jaql is a functional, declarative query language designed to process large data sets. It permits SQL programmers to develop Hive Query Language (HQL) statements akin to typical SQL statements. Hive is a runtime Hadoop support architecture that leverages Structure Query Language (SQL) with the Hadoop platform. It is comprised of two key modules: the language itself, called PigLatin, and the runtime version in which the PigLatin code is executed. Pig programming language is configured to assimilate all types of data (structured/unstructured, etc.). HDFS, MapReduce, Hive, Oozie, Pig, Impala, Solr) easy to use and accessible from your browser (e.g. When tasks are executed, MapReduce tracks the processing of each server/node. MapReduce provides the interface for the distribution of sub-tasks and the gathering of outputs. Understand big data, challenges, distributed environment. It divides the data into smaller parts and distributes it across the various servers/nodes. It also covers various Eco-Systems (Hive, Pig, Sqoop, Flume). HDFS enables the underlying storage for the Hadoop cluster. The Hadoop Distributed File System (HDFS)
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