What is Hive?

Hive is a data warehouse software wherein for providing data query and analysis. Hive gives a SQL-like interface the data stored in various databases and file systems. Hive provides the necessary SQL removal to detach SQL-like queries into the underlying Java without the need to implement queries in the low-level Java.While initially developed by Facebook, Apache Hive is used and developed by other companies like Netflix and the Financial Industry Regulatory Authority(FINRA).

Provide acceleration, index type including compaction and bitmap index as of 0.10, more index types are planned. Different storage varieties like plain text, RCFile, HBase, ORC, and others. Significantly reducing the time to perform linguistics checks throughout question execution.Operating on compressed data.Built-in user-defined functions to govern dates, strings, and other data-mining tools. The storage and querying operations of Hive closely resemble to those of the traditional databases.

While Hive could be a SQL non-standard speech, there are a lot of differences in structure and working of Hive in comparison to relational databases. The variations square measure primarily as a result of Hive is constructed on high of the Hadoop scheme, and needs to suits the restrictions of Hadoop and MapReduce. A schema is applied to a table in ancient databases. In such ancient databases, the table typically enforces the schema when the data is loaded into the table.

This enables knowledge|theinfo|the information}base to create certain that the data entered follows the illustrationof the table as such as by the table definition. This design is called schema on write. In comparison, Hive interview questions ; doesn’t verify the information against the table schema on write. Instead, it later on will run time checks once the information is scan. This model is called schema on read. The two approaches have their own advantages and disadvantages.

1. Checking knowledge against table schema throughout the load time adds additional overhead, which is why traditional databases take a longer time to load data.
2. Quality checks square measure performed against the information at the load time to confirm that the informationisn’t corrupt.
3. Early detection of corrupt knowledge ensures early exception handling.
4. Since the tables are forced to match the schema after or during the data load, it has better query time performance.
5. Hive, on the opposite hand, can load data dynamically without any schema check, ensuring a fast initial load, but with the drawback of comparatively slower performance at query time.
6. Hive does have an advantage when the schema is not available at the load time, but is instead generated later strongly.

With Hive v0.7.0’s integration with Hadoop security, the issues have largely been fixed. TaskTracker jobs are run by the user who launched it and the username can no longer be spoofed by setting the hadoop.job.ugi property. Permissions for newly created files in Hive are dictated by the HDFS. The Hadoop distributed file system authorization model uses three entities: user, group and others with three permissions: read, write and execute. With Hive in place you can rest assured that all of your warehouse accounting needs will be taken care of.