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. Workbench 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.