Table metadata in Hive is:
By default, hive use an embedded Derby database to store metadata information. The metastore is the 'glue' between Hive and HDFS. It tells Hive where your data files live in HDFS, what type of data they contain, what tables they belong to, etc.
The Metastore is an application that runs on an RDBMS and uses an open source ORM layer called DataNucleus, to convert object representations into a relational schema and vice versa. They chose this approach as opposed to storing this information in hdfs as they need the Metastore to be very low latency. The DataNucleus layer allows them to plugin many different RDBMS technologies.
Note:
* By default, Hive stores metadata in an embedded Apache Derby database, and other client/server databases like MySQL can optionally be used.
* features of Hive include:
Metadata storage in an RDBMS, significantly reducing the time to perform semantic checks during query execution.
You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS?
Review the following data and Pig code:
What command to define B would produce the output (M,62,95l02) when invoking the DUMP operator on B?
