Apache Hive supports analysis of large datasets stored in Hadoop's HDFS and compatible file systems such as Amazon S3 filesystem. It provides an SQL-like language called HiveQL with schema on read and transparently converts queries to map/reduce, Apache Tez[7] and Spark jobs. All three execution engines can run in Hadoop YARN. To accelerate queries, it provides indexes, including bitmap indexes.
Few features:-
1.Indexing to provide acceleration, index type including compaction and Bitmap index as of 0.10, more index types are planned. 2.Different storage types such as plain text, RCFile, HBase, ORC, and others. 3.Metadata storage in an RDBMS, significantly reducing the time to perform semantic checks during query execution. 4.Operating on compressed data stored into the Hadoop ecosystem using algorithms including DEFLATE, BWT, snappy, etc. 5.Built-in user defined functions (UDFs) to manipulate dates, strings, and other data-mining tools. Hive supports extending the UDF set to handle use-cases not supported by built-in functions. 6.SQL-like queries (HiveQL), which are implicitly converted into MapReduce or Tez, or Spark jobs.
