Signal Hub capabilities enable end-to-end analytics workflow.

Signal Hub capabilities enable end-to-end analytics workflow.




Signal Hub consists of four integrated components that collaborate seamlessly to operationalize end-to-end Big Data advanced analytics for the enterprise.

Source Data

Signal Hub™

Integrated Development Environment (IDE)

Knowledge Center (KC)

Signal Hub Manager

Signal Hub Server

  • Upstream Connections
  • Workflow Server
  • Data Flow Engine
  • Operational Graph Database
  • Downstream Connectors


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Hosting Options

Customer Hosting
Opera Solutions Hosting
Virtual Private Cloud Hosting

Enterprise Applications

Execution Systems

Key Components and Functionality

  • Data Ingestion. With its prebuilt ETL functionality, Signal Hub allows users to upload and normalize data from structured and unstructured data sources, such as SQL-based RDBMS, HDFS, Cassandra, and many more.
  • Signal API. The IDE features an easy-to-use declarative language that easily identifies data sets and codifies the associated Signals.
  • Execution Engine. Managing the data flow across the entire analytics workflow, the execution engine provides seamless integration between stages.
  • Signal Library. This repository of ready-to-use Signals is based on real-world business use cases that users can apply directly to their data.
  • External Code Integration & Augmentation. Prebuilt code in Python, R, and other popular languages allows users to extend their library and data manipulation possibilities.
  • Modeling Tools Plug-in. Interoperate with R, SAS, IBM SPSS, and other popular analytical tools by consuming existing models built in any PMML-compliant format.
  • Visualization Tools. Signal Hub provides native visualization and extends that visualization to BI tools, enterprise applications, or execution systems to which you export data from Signal Hub.
  • External APIs. An open API allows you to service data requests from external applications.
  • Third-Party Data Enrichment. Built-in hooks interface with third-party data sources, allowing users to combine external data (third-party proprietary or publicly available) with data that your business owns and generates, enabling more sophisticated and accurate analysis.

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