Hive, on the other hand, is not exactly a database but a data warehousing package built atop Hadoop. HBase is fundamentally a column-oriented, distributed NoSQL database that runs on top of the Hadoop Distributed File System (HDFS) and provides a fault-tolerant way to store sparse data sets, which are common in big data use cases. – Although HBase and Hive are both Hadoop based data warehouse structures used to store and process large amounts of data, they differ significantly as to how they store and query data. However, it cannot be used for real time processing of data.ĭifference between HBase and Hive Technology Data can be read and written from Hive and HBase and vice-versa. Hive is basically a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large data sets stored in Hadoop compatible file systems. It allows you to query the semi-structured data stored in Hadoop, which is eventually turned into a MapReduce job, executed either locally or on a distributed MapReduce cluster. Hive is a different technology than HBase it structures the data in a set of tables that can be joined, aggregated and queried upon using a query language called Hive Query Language (HQL) that is very similar to the SQL, used for batch processing of big data. Hive is not exactly a database but a data warehousing package built atop Hadoop. However, it is not designed to perform aggregations of the data. It allows quick reads of random access data from large amounts of data based on the key values.
It provides a way to store sparse data sets, which are common in big data use cases. It sits on Apache Hadoop and powered by a fault-tolerant distributed file structure known as the HDFS.
#Hbase storage policy disk archive software#
It is designed and developed by many engineers under the framework of Apache Software Foundation. HBase is fundamentally a column-oriented, distributed NoSQL database that runs on top of the Hadoop Distributed File System (HDFS). HBase is an open-source, non-relational, database management system inspired by the Google’s Big Table architecture and written in Java. Although, both HBase and Hive are used as data stores to store unstructured data, they are different. This takes the unnecessary effort of having to write MapReduce code. Hive offers an SQL-like query language that allows you to query the semi-structured data stored in Hadoop. Hive, on the other hand, is more like a traditional data warehouse reporting system that runs on top of Hadoop.
For example, if you need to filter through a huge store of emails to pull out one for auditing or for any other purpose, this will be a perfect use case for HBase. HBase is a preferred choice to handle large amounts of data. This is where HBase comes to the picture. Managing and processing huge volumes of web-based data are becoming increasingly difficult via conventional database management tools. HBase and Hive are both Hadoop based data warehouse structures that differ significantly as to how they store and query data.
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