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Apache Hive is a data warehouse technology that facilitates querying and managing of large datasets stored in distributed storage systems like Hadoop. Hive is built on top of Hadoop and provides a SQL-like language called HiveQL (HQL) to query data stored in Hadoop Distributed File System (HDFS) or other data sources like Apache HBase.
Hive translates SQL-like queries written in HiveQL into MapReduce or Apache Tez jobs that can be executed on a Hadoop cluster. By doing so, Hive enables data analysts and scientists to perform complex analysis and data processing tasks on large datasets with familiar SQL-based tools and techniques. Hive also provides a schema-on-read approach, which means that data schemas are applied when querying data rather than when data is ingested into the system, offering more flexibility and agility over traditional data warehousing techniques.
Apache Hive offers the following benefits:
Hive consists of the following components:
While Hive offers several advantages, it also has some limitations:
Apache Hive is a powerful tool for data warehousing and analysis on a Hadoop cluster. Its SQL-like interface and schema-on-read approach make it easy for data analysts and scientists to query and process large datasets using familiar tools and techniques. While Hive has some limitations, its benefits far outweigh its drawbacks for many businesses and organizations.
Dremio enables users to run federated queries across multiple data sources like Apache Hive, HDFS, AWS S3 and many others. By knowing how to query data with Apache Hive, Dremio users can leverage the power of Hive for data processing and analysis in their federated queries, further increasing the functionality of Dremio.