Apama

Imagine how responsive your enterprise could be if you could glean real-time insights from all that big fast data—data streaming in from global markets, mobile devices, the Internet of Things (IoT), internal transactional systems and a myriad of other sources. You can be that event-driven enterprise by using Apama. Software AG's Apama Streaming Analytics—supporting predictive analytics—is the world’s #1 platform for streaming analytics and intelligent automated action on fast-moving big data. With Apama, you can analyze and act on high-volume business operations in real time.
Apama Streaming Analytics is built on an in-memory architecture that enables real-time processing of extremely fast, large data volumes—orders of magnitude larger than traditional database-based IT architectures.

Data science teams build predictive models in whatever data mining tools they prefer to use, then use Predictive Analytics for Apama to load the models in the PMML format. This step takes a fraction of the time that model deployment ordinarily requires, and eliminates the need for manual coding, cross-checking and error correction. Apama allows for ingesting these models rapidly, making them instantly organic to the business process that they support, as defined by various Apama applications.

Apama then probes incoming event data from any device, social media stream or business system with extremely low latency against the imported predictive models for real-time scoring. Predictive Analytics for Apama analyzes this streaming data, which can also be enriched with historic and contextual data-at-rest where necessary, to identify business patterns that have happened or are likely to happen.

The platform’s visualizations and visual analytics for business users support both human-oriented and automated intelligent actions, alerts and notifications.

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  • Apama Streaming Analytics is built on an in-memory architecture that enables real-time processing of extremely fast, large data volumes—orders of magnitude larger than traditional database-based IT architectures.

    Data science teams build predictive models in whatever data mining tools they prefer to use, then use Predictive Analytics for Apama to load the models in the PMML format. This step takes a fraction of the time that model deployment ordinarily requires, and eliminates the need for manual coding, cross-checking and error correction. Apama allows for ingesting these models rapidly, making them instantly organic to the business process that they support, as defined by various Apama applications.

    Apama then probes incoming event data from any device, social media stream or business system with extremely low latency against the imported predictive models for real-time scoring. Predictive Analytics for Apama analyzes this streaming data, which can also be enriched with historic and contextual data-at-rest where necessary, to identify business patterns that have happened or are likely to happen.

    The platform’s visualizations and visual analytics for business users support both human-oriented and automated intelligent actions, alerts and notifications.
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