Apache Hive Pricing & Demo

The USP of Apache Hive ETL Software is how flexible and easy-to-integrate it is. Not only can it be used for several functions, but it can also be used with a number of file systems that integrate with Hadoop. These include  MapR Data Platform, MapR XD, and MapR Database. It is also one of the best software in the market for quick and efficient data querying, summarization, and analysis.

Request Apache Hive Pricing to get more information.

Apache Hive ETL Software provides data query and analysis. It is essentially an open-source data warehouse software project and enables an SQL-like interface to sort through enormous quantities of data stored on Hadoop. It is mostly used by researchers and programmers who need to sift through and analyze a lot of data quickly and efficiently. 

Apache Hive Pricing

Apache pricing plans have an annual as well as monthly options. For the monthly option, users pay $16 per user. In the annual Hive pricing plan, users pay $12 a month. Base plans include summary views, chat, meeting notes, automated workflows, flexible project layouts, analytics, My Actions, and Hive Mail.

Apache Hive Demo

A free trial of 14 days is offered to all users, after which the pricing plans are implemented, depending on individual user needs. Apache Hive ETL Software can be integrated with a number of systems and file sets, which makes it very convenient for data analysts, enterprise data architects, data warehouse teams, and IT storage administrators, amongst others. 

Features

Enables easy data summarization and analysis
Summarise and analyze large datasets and ad-hoc queries stored across several databases and file systems. Integrates with the MapR Data Platform, MapR XD, Map R Database.

Allows users to structure large sets of unstructured data
It provides a hassle-free way of structuring data and performing batch-like SQL queries on data. With the familiar JBDC/OBDC interface, Hive can integrate easily with traditional data center technologies, further easing the structuring process.

HBase to store Hive Metadata
Queries only take a few seconds to run, with additional support from Tez and Spark engines. Real-time read or write access to data in HDFS.

Hive on Spark Container
Spark Container enables in-memory computations and transformations. Spark provides an additional engine for data execution.