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Beginners Guide Towards Understanding Apache Hive
Apache Hive can be interpreted as an advanced data warehouse system which is built on top of Apache Hadoop. Apache Hive supports advanced applications involving data summarization, ad-hoc queries, and also helps in the process of analyzing enormous datasets that are stored in various databases. Apart from these applications, Hive also supports file systems that integrate with Hadoop, including the MapR Data Platform with MapR XD and MapR Database.
By making use of Hive, analysts will find it a lot easier to apply structure to large amounts of unstructured data so as to perform batch SQL-like queries on that data. Another benefit of using Hive is that it can easily integrate with other traditional data center technologies using the familiar JDBC/ODBC interface.
What Makes Apache Hive So Dominant?
Until the advent of Hive, Big Data experts had to deal with MapReduce which is a scalable, resilient distributed processing framework. Popular search engines like Google made use of MapReduce for indexing large volumes content from millions of websites.
By using MapReduce, analyzing large volumes of semi-structured and unstructured data in Hadoop can be made possible but however the problem here is that it is extremely difficult to program the MapReduce Java API especially for professionals who are from non programming background.
This is where Hive comes into the play. It was initially developed by at Facebook for analyzing large amounts of data stored on a distributed file system. Even professionals from the non-programming background will find it easy to read, write, and manage large datasets residing in distributed Hadoop storage using HiveQL SQL-like queries. This is one among the prime reasons that lead to the rise in the popularity for Hive.
Advantages Of Hive-
Let’s have a look at the advantages of Hive
- Higher level query language – Simplifies working with large amounts of data
- Lower learning curve than Pig or MapReduce
- It is highly reliable, avialable, resistant to node failures, commodity hardware and more
- It has a rule based optimizer for optimizing logical plans
- Metastore or Metadata store is another specific feature of Hive architecture which makes the lookup easy
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