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What is DataHub?
DataHub stands out as a dynamic open-source metadata platform designed to improve data discovery, observability, and governance across diverse data landscapes. It allows organizations to quickly locate dependable data while delivering tailored experiences for users, all while maintaining seamless operations through accurate lineage tracking at both cross-platform and column-specific levels. By presenting a comprehensive perspective of business, operational, and technical contexts, DataHub builds confidence in your data repository. The platform includes automated assessments of data quality and employs AI-driven anomaly detection to notify teams about potential issues, thereby streamlining incident management. With extensive lineage details, documentation, and ownership information, DataHub facilitates efficient problem resolution. Moreover, it enhances governance processes by classifying dynamic assets, which significantly minimizes manual workload thanks to GenAI documentation, AI-based classification, and intelligent propagation methods. DataHub's adaptable architecture supports over 70 native integrations, positioning it as a powerful solution for organizations aiming to refine their data ecosystems. Ultimately, its multifaceted capabilities make it an indispensable resource for any organization aspiring to elevate their data management practices while fostering greater collaboration among teams.
What is DataHUB+?
DataHUB+ represents an advanced process data historian and visualization solution for modern enterprises. It empowers users to monitor assets and systems in real-time across extensive networks, providing quick insights through integrated analytics and visualization tools, enabling visibility into the status of any facility, plant, or system at any given moment.
Being agnostic to equipment and sensors, DataHUB+ can effortlessly import data from any IoT devices or sensors, regardless of whether the data is structured or unstructured. This adaptability allows DataHUB+ to establish itself as the definitive source of truth, ensuring all operational data is securely and reliably stored. Additionally, the platform proactively assesses data quality prior to ingestion and notifies teams of any issues that arise.
Unlike traditional process historians that depend on expensive IT infrastructure, DataHUB+ is designed for easy scalability to meet the demands of enterprise data management. Furthermore, it integrates seamlessly with VROC's AI solution OPUS, facilitating forecasting, predictive maintenance, and sophisticated analytics.
By removing data silos and minimizing data wrangling within organizations, DataHUB+ enhances data-driven decision-making processes. Organizations can harness DataHUB+ to gain deeper insights into their operations and drive significant improvements in efficiency.
Integrations Supported
Active Directory
Amazon Athena
Amazon Redshift
Apache Airflow
Apache Kafka
Apache Superset
Aspen HYSYS
Azure Industrial IoT
ClickHouse
Dagster+
Integrations Supported
Active Directory
Amazon Athena
Amazon Redshift
Apache Airflow
Apache Kafka
Apache Superset
Aspen HYSYS
Azure Industrial IoT
ClickHouse
Dagster+
API Availability
Has API
API Availability
Has API
Pricing Information
Free
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
DataHub
Company Location
United States
Company Website
datahubproject.io
Company Facts
Organization Name
VROC
Date Founded
2016
Company Location
Australia
Company Website
vroc.ai/products/datahub/
Categories and Features
Data Discovery
Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics
Data Governance
Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management