Imarticus Learning
India’s leading professional education institute, offering certified industry-endorsed training in Financial Services, Investment Banking, Business Analysis, IT, Business Analytics & Wealth Management
Hive or Apache Hive as it happens to be professionally known, is basically a data warehouse software project which is built as a superstructure on the base of Apache Hadoop. The main purpose for the conceptualization of Hive is in order to provide various data related services such as data summarization, query and analysis. Hive usually happens to give a very SQL type of an interface in order to query data that happens to be stored in various databases and file systems that happens to integrate with the data analytical programming tool of Hadoop Programming.
Usually according to the earlier standards, all of the SQL type of data base queries were supposed to be implemented into the system of MapReduce Java and API in order to execute the operations and queries over the distributed data. This is what changed with the arrival of Hive on the programming scene. Herein the required SQL abstraction is provided by Hive itself in order to integrate these similar queries directly in to JAVA without the need of implementation of queries along the low level of JAVA API. Did you know that Hive was initially developed by the social networking giant called Facebook? What is more interesting is that hive was also used by various other companies when it came to programming like Netflix and also the Financial Industry Regulator Authority. Even the delivery giant Amazon makes use of Hive as a part of its software fork which basically includes a software environment called Amazon Elastic MapReduce on the web services provided by Amazon. The various feature of Hive in Hadoop include accelerating the index types and index compaction and many other indexes like bitmap index and many more types that have been planned of and will eventually be implemented. Under it there are a varied number of storage types such as plain text, RCfile, HBase, ORC and so on. It also helps in order to store Metadata and this helps in also reducing the significant amount of time that is required to perform semantic checks during query execution. In order to operate on compressed data that is stored in Hadoop, this Hive ecosystem comes to be of great use as it makes use of various algorithms which include DEFLATE, BWT, snappy and so on. As a part of Hive, the user will also get to access built in user defined functions which are commonly known as UDFs and are generally used in order to manipulate dates, strings and various other data mining tools. Hive supports the extension of this very UDF set to various other functions which are usually not supported by the essentially in built functions. SQl queries as a part of Hive are directly converted into MapReduce or Tez or even Spark Jobs. This very Hive has thus become insanely popular as a part of the Hadoop data analytical tool environment and is highly preferred by many data aspirants. Many of these aspirants thus tend to go ahead and get trained through professional training institutes like Imarticus learning that offer courses in Hive in Hadoop.
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About ImarticusImarticus Learning is a education institute based in Mumbai. We offer certified industry-endorsed training in Financial Services, Investment Banking, Business Analysis, IT, Business Analytics & Wealth Management. Archives
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