Ratings and Reviews 86 Ratings
Ratings and Reviews 1 Rating
What is HiveMQ?
HiveMQ provides the most trusted IoT data streaming and Industrial AI platform, built on MQTT, to power a reliable, scalable, and AI-ready data backbone.
What HiveMQ is known for:
1. MQTT-native: Built around the MQTT standard, purpose-designed for event-driven, real-time communication
2. Enterprise-grade reliability: Handles millions of concurrent connections with high availability and fault tolerance
3. Industrial-ready: Widely used in IIoT, manufacturing, automotive, energy, smart infrastructure, and data centers
4. Scalable & secure: Supports global deployments with strong security, governance, and observability
5. UNS & IT/OT convergence enabler: Commonly used as the backbone for Unified Namespace architectures and seamlessly connects OT devices with IT systems for full visibility and interoperability.
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.
Integrations Supported
Airtool
Amadea
Apache Avro
Aqua Data Studio
Ataccama ONE
DBeaver Community
Data Virtuality
Dataiku
Datameer
DigDash
Integrations Supported
Airtool
Amadea
Apache Avro
Aqua Data Studio
Ataccama ONE
DBeaver Community
Data Virtuality
Dataiku
Datameer
DigDash
API Availability
Has API
API Availability
Has API
Pricing Information
$0.34/hour
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
HiveMQ
Date Founded
2012
Company Location
Germany
Company Website
www.hivemq.com
Company Facts
Organization Name
Apache Software Foundation
Date Founded
1999
Company Location
United States
Company Website
hive.apache.org
Categories and Features
Industrial IoT
Condition Monitoring
Data Visualization
Factory Data Analytics
Machine Learning
Machine Workflow Creation
Predictive Maintenance
Production Line / Factory Insights
Real-Time Monitoring
Reporting / Analytics
Smart Alerts / Notifications
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization
Message Queue
Asynchronous Communications Protocol
Data Error Reduction
Message Encryption
On-Premise Installation
Roles / Permissions
Storage / Retrieval / Deletion
System Decoupling
Categories and Features
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control