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What is Hive Engine?

Hive Engine is an innovative platform that empowers various stakeholders, including communities, project creators, fundraisers, developers, and businesses, to seamlessly build on the Hive blockchain like never before. Until this point, your experience with Hive's vast capabilities has only skimmed the surface, much like using a sophisticated tool merely to charge a smartphone. By adding a new layer of functionality, Hive Engine paves the way for limitless opportunities that harmoniously integrate with the existing blockchain infrastructure. The platform actualizes the concept of smart contracts on the Hive blockchain, starting with the quick and easy creation of custom tokens. We aimed to make Tokens available to you rapidly, and there are many thrilling advancements on the way. These upcoming features will include enhanced token management tools and a dedicated marketplace for trading tokens against Hive, akin to the existing HIVE/SBD market. This forward-thinking model simplifies the deployment process, making it accessible to everyone, regardless of their technical background. With Hive Engine, the possibilities for blockchain innovation are just beginning to unfold, and we are excited to see how users will harness this potential. As we continue to evolve, we look forward to creating a vibrant ecosystem that fosters creativity and collaboration.

What is Apache Hive?

Apache Hive serves as a data warehousing framework that empowers users to access, manipulate, and oversee large datasets spread across distributed systems using a SQL-like language. It facilitates the structuring of pre-existing data stored in various formats. Users have the option to interact with Hive through a command line interface or a JDBC driver. As a project under the auspices of the Apache Software Foundation, Apache Hive is continually supported by a group of dedicated volunteers. Originally integrated into the Apache® Hadoop® ecosystem, it has matured into a fully-fledged top-level project with its own identity. We encourage individuals to delve deeper into the project and contribute their expertise. To perform SQL operations on distributed datasets, conventional SQL queries must be run through the MapReduce Java API. However, Hive streamlines this task by providing a SQL abstraction, allowing users to execute queries in the form of HiveQL, thus eliminating the need for low-level Java API implementations. This results in a much more user-friendly and efficient experience for those accustomed to SQL, leading to greater productivity when dealing with vast amounts of data. Moreover, the adaptability of Hive makes it a valuable tool for a diverse range of data processing tasks.

Media

Media

Integrations Supported

Acceldata
Amadea
Amundsen
Apache Avro
Apache Hudi
Ascend
CelerData Cloud
Flyte
IRI Data Protector Suite
Microsoft Power Query
Nightfall
Nucleon Database Master
Precisely Connect
Secoda
SecuPi
Timbr.ai
Truedat
WEBDEV
lakeFS

Integrations Supported

Acceldata
Amadea
Amundsen
Apache Avro
Apache Hudi
Ascend
CelerData Cloud
Flyte
IRI Data Protector Suite
Microsoft Power Query
Nightfall
Nucleon Database Master
Precisely Connect
Secoda
SecuPi
Timbr.ai
Truedat
WEBDEV
lakeFS

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Hive Engine

Date Founded

2020

Company Website

hive-engine.com

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

hive.apache.org

Categories and Features

Categories and Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

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