Hive Course And Certification

What is Hive?

Hive is a data warehouse infrastructure tool that is used to process data that is structured in Hadoop. It lives on top of Hadoop to abstract Big Data and makes querying and analyzing of the data warehouse very easy.

Hive is not a relational database system and that is why it is not suitable for online transaction processing and real-time queries with row-level updates. Hive is designed and developed for carrying out online analytical processing or OLAP. It further presents developers with a query language known as HiveQL. It transforms the queries that are looking almost like SQL into MapReduce jobs for easy execution and for the processing of a large amount of data. Apache hive is one of the components of Hadoop that are frequently used by data analysts whereas apache pig is also made used for the same task, but it is mostly used by researchers and computer programmers.

Apache hive is an open-source data warehousing software system used to query and analyze large amounts of data sets that are stored in Hadoop storage. Hive is entirely suited for running batch jobs and not for carrying out online transactional processing work types. It also does not have the support of real-time database queries. Hive makes use of SQL like query language and it is mainly used for generating reports. Hive is generally deployed on the server-side and it offers support for structured data. Hive also supports the integration with JDBC and BI software tools.

Features of Hive

There are many features of Hive and some of them are:

1. Meta Store: The repository that holds the metadata is called the hive meta store. The metadata is made up of the different data about the tables like its schema, location, various information about the partitions that help to monitor unpredictably distributed data progress in the cluster.

 2. Driver: On the execution of the Hive query language statement, the driver accepts the statement and it controls it for the full cycle of execution. Together with the execution of the statement, the driver also collects and stores the metadata that is generated from the execution. It also produces sessions to monitor the progress and life cycle of various executions.

3. Compiler: The compiler is used for converting or translating the Hive query language into its equivalent MapReduce input. It calls/invokes a method that carries out the steps and tasks that are required to read the HiveQL output as required by the MapReduce function.

4. Optimizer: The main function of the optimizer is to keep improving the efficiency and the scalability of creating a task while modifying the data before the reduce operation is carried out. It also carries out various transformations like aggregation and pipeline conversion by a single join for several other joins.

5. Executor: After a successful compilation and optimization step, the main function of the executor is to perform the tasks. The executor is used to interact with the Hadoop job tracker for scheduling tasks and to ready them to run.

Benefits of Hive

There are many benefits of Hive, and some of them are:

1. Hive stores your application schema in a database and your processed data into HDFS.

2. Hive is designed for carrying out OLAP faster and efficiently.

3. Hive provides a language that is like SQL for querying the database known as HiveQL or HQL.

4. Hive is fast, familiar, scalable, and highly extensible.