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Layer 4 of the Big Data Stack: Analytical Data Warehouses

Mine Big Data with Hive

Hive is a batch-oriented, data-warehousing layer built on the core elements of Hadoop (HDFS and MapReduce) and is very useful in big data. It provides users who know SQL with a simple SQL-lite implementation called HiveQL without sacrificing access via mappers and reducers. With Hive, you can get the best of both worlds: SQL-like access to structured data and sophisticated big data analysis with MapReduce.

Unlike most data warehouses, Hive is not designed for quick responses to queries. In fact, queries can take several minutes or even hours depending on the complexity. As a result, Hive is best used for data mining and deeper analytics that do not require real-time behaviors. Because it relies on the Hadoop foundation, it is very extensible, scalable, and resilient, something that the average data warehouse is not.

Hive uses three mechanisms for data organization:

  • Tables: Hive tables are the same as RDBMS tables consisting of rows and columns. Because Hive is layered on the Hadoop HDFS, tables are mapped to directories in the file system. In addition, Hive supports tables stored in other native file systems.

  • Partitions: A Hive table can support one or more partitions. These partitions are mapped to subdirectories in the underlying file system and represent the distribution of data throughout the table. For example, if a table is called autos, with a key value of 12345 and a maker value Ford, the path to the partition would be /hivewh/autos/kv=12345/Ford.

  • Buckets: In turn, data may be divided into buckets. Buckets are stored as files in the partition directory in the underlying file system. The buckets are based on the hash of a column in the table. In the preceding example, you might have a bucket called Focus, containing all the attributes of a Ford Focus auto.

Hive metadata is stored externally in the “metastore.” The metastore is a relational database containing the detailed descriptions of the Hive schema, including column types, owners, key and value data, table statistics, and so on. The metastore is capable of syncing catalog data with other metadata services in the Hadoop ecosystem.

Hive supports an SQL-like language called HiveQL. HiveQL supports many of the SQL primitives, such as select, join, aggregate, union all, and so on. It also supports multitable queries and inserts by sharing the input data within a single HiveQL statement. HiveQL can be extended to support user-defined aggregation, column transformation, and embedded MapReduce scripts.

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