Since its incubation in 2008, Apache Hive is considered the defacto standard for interactive SQL queries over petabytes of data in Hadoop. And with the completion of the Stinger Initiative, and the first phase of Stinger.next, the Apache community has greatly improved Hive’s speed, scale and SQL semantics. Throughout all the innovation, Hive easily integrates with other critical data center technologies using a familiar JDBC interface.
Stinger.next: Hortonworks Focus for Apache Hive
The Stinger Initiative successfully delivered a fundamental new Apache Hive, which evolved Hive’s traditional architecture and made it faster, with richer SQL semantics and petabyte scalability. We continue to work within the community to advance these three key facets of hive:
Stinger.next is focused on the vision of delivering enterprise SQL at Hadoop scale, accelerating the production deployment of Hive for interactive analytics, reporting and and ETL. More explicitly, some of the key areas that we will invest in include:
|Spark Machine Learning Integration||
Focus for Innovation
|Transactions with ACID semantics||
Recent Hive Releases
|Apache Hive Version||Prior Enhancements||0.14||
What Hive Does
Hadoop was built to organize and store massive amounts of data of all shapes, sizes and formats. Because of Hadoop’s “schema on read” architecture, a Hadoop cluster is a perfect reservoir of heterogeneous data—structured and unstructured—from a multitude of sources.
Data analysts use Hive to explore, structure and analyze that data, then turn it into business insight.
Here are some advantageous characteristics of Hive for enterprise SQL in Hadoop:
|Scalable and Extensible||
How Hive Works
The tables in Hive are similar to tables in a relational database, and data units are organized in a taxonomy from larger to more granular units. Databases are comprised of tables, which are made up of partitions. Data can be accessed via a simple query language and Hive supports overwriting or appending data.
Within a particular database, data in the tables is serialized and each table has a corresponding Hadoop Distributed File System (HDFS) directory. Each table can be sub-divided into partitions that determine how data is distributed within sub-directories of the table directory. Data within partitions can be further broken down into buckets.
Hive supports all the common primitive data formats such as BIGINT, BINARY, BOOLEAN, CHAR, DECIMAL, DOUBLE, FLOAT, INT, SMALLINT, STRING, TIMESTAMP, and TINYINT. In addition, analysts can combine primitive data types to form complex data types, such as structs, maps and arrays.
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