Signal Hub consists of four integrated components that collaborate seamlessly to operationalize end-to-end Big Data advanced analytics for the enterprise.
Integrated Development Environment (IDE)
Knowledge Center (KC)
Signal Hub Manager
Signal Hub Server
Data Flow Engine
Operational Graph Database
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.