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Apache Hive is a data warehousing and SQL-like query language for Hadoop, allowing users to analyze and process large datasets.
Hive works on top of Hadoop by creating a metastore that contains metadata about the data stored in HDFS. It then allows users to run SQL-like queries against this metadata.
Key features include support for SQL-like queries, data warehousing, and ETL (Extract, Transform, Load) capabilities.
Hive is both - it's a query engine that allows users to write SQL-like queries, but it also provides a way for developers to create custom code using various programming languages (e.g. Java, Python).
Benefits include increased data accessibility, simplified query writing, and improved performance.
Hive is designed to handle very large datasets by leveraging the distributed processing capabilities of Hadoop. It can process petabytes of data with relative ease.
While Hive is great for batch processing, it's not ideal for real-time analytics. For that purpose, you may want to consider other tools like Apache Storm or Apache Flink.
Yes, Hive can be integrated with a range of other big data tools and frameworks, including HBase, Pig, and Mahout.
Getting started involves downloading the software, installing it on your cluster, and learning how to write SQL-like queries against your Hadoop data.
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