Apache Hive is described to be a data warehouse software project which is created on top of the Apache Hadoop for the process of providing data query as well as analysis to the users and the developers. Hive can give an SQL-like interface to the data of the query which is stored in different databases as well as file systems leather capable of integrating with the Hadoop. The traditional queries of the SQL can be implemented in the map-reduce Java API to execute the application of the SQL as well as the queries over the data which is distributed. Hive can provide the necessary and important abstraction of the SQL the users and the developers of java without the need to implement the queries at the level of Java API. Since a lot of the data warehousing applications are working with the SQL best query languages and in this case, it has the ability to Aid the portability of open applications that are SQL-based to the Hadoop. Initially, it was developed by Facebook, and Hive was used and developed by other companies like a regulatory Authority as well as Netflix and so on. The Apache Hive can support the process of analyzing the large data sets that are stored in the Hadoop and they are compatible with file systems such as Amazon S3 and it provides a query language which is like SQL and it is called HiveQL. It provides a schema on the reader as well as a transparently convert the queries do nothing but map-reduce. The Apache Hive is described to be distributed as well as a fault-tolerant data warehouse system that can permit the users and the developers along with the analytics on a huge scale. A data warehouse in this case can provide a central scale for information that can easily be analyzed to make informed as well as decisions that are mainly data-driven. Hive permits its users and developers to read as well as write and to even manage the petabytes of the data with the use of the SQL. It should be added that Apache hive is an open-source project which is run by main volunteers at the software Foundation of the Apache. Hive is used for the process of quarrying so that it can help the user and the developer to describe the questions that the user desire to be answered but it does not have the ability to control how the question will be answered.
Features and attributes of the CData Drivers for Apache Hive
It has the following features and attributes that are stated below such as:
It has a built-in user which is defined functions and they can manipulate the dates, string as well as other data mining tools that are present.
Hive can support the process of extending the UDF to set to the handle which is not supported by the function of built-in.
It has queries that are SQL like and there are implicitly converted into the Sparks jobs as well as map-reduce and even the Tez.
It can operate on the compressed data which is stored in the Hadoop ecosystem with the help and use of the algorithms that include DEFLATE, BWT as well as snappy.
Hive has the capability to store metadata in the embedded database of Apache Derby as well as the other client-server databases such as MySQL and it can be used optionally.
It consists of different storage types such as plain text, ORC, HBase, and a lot more.
BI & Data Visualization
The drivers are offering the fastest and the easiest way for the users to connect to the real-time Hive data with the help of analytics, business intelligence, Advent of data visualization technology.
Workflow & Automation
It can connect to the hive from the famous data migration as well as tools such as the BPM, iPaaS, and the ESB. The drivers in this case are capable of providing straightforward access to the data of the Hive.
The drivers are capable of providing a virtual database abstraction to the users and the developers which are on the top of the hive and can support popular data virtualization features like the predicate pushdown as well as query delegation.