SAS^® EVENT STREAM PROCESSING

Analyze streaming data, uncover hidden insights with AI and make real-time, intelligent decisions in the environment of your choice.

Read IDC's Market Note "SAS Dives Deeper into IoT Analytics"

Use machine learning and streaming analytics to uncover insights at the edge and make real-time, intelligent decisions in the cloud.

Built for speed

Engineered for high-volume processing of millions of events per second and low-latency response times on GPU, CPU and other commodity hardware.

Ingests & consolidates streaming IoT sources

Extensive suite of prebuilt connectors and adapters lets you consume today's IoT sources – including cloud or on-site streams – and extend in the future.

Ready for real-time action

Customizable alerts, notifications and updates provide situational awareness so you can react appropriately to what's happening or predicted to happen.

Flexible, open modeling environment

Intuitive visual interface makes it easy to define, test and refine models in a low code environment. Data scientist-friendly coding environments include familiar Python developer (e.g., Jupyter notebook).

Complete multiphase analytics

Embed SAS at the edge, in the fog and in the cloud, cleansing and analyzing data at each streaming event phase. In-depth SAS models are portable to the stream and the edge. Supports algorithms, for example, to perform support vector data description, robust principal component analysis, random forest, gradient boosting and streaming regression analysis.

Image & video analytics

Combines streaming analytics, video and image ingestion with powerful neural networks to process video and still image data, including image pre-processing and object detection and classification.

Cloud native

Compatible with cloud technologies, including Docker, Kubernetes and BOSH Cloud Foundry, for large-scale, elastic, multitenant, distributed services. Ensures continuous, secure and stable event pattern detection through patented, instantaneous 1+N way failover, native failover, guaranteed delivery without persistence and other fault-tolerance functions for resilient and highly available event stream processing.

In-stream learning model windows

Use different window types – Train, Score, Calculate, Model Supervisor, Model Reader – for different tasks (e.g., specifying data stream input sources, patterns of interest and derived output actions).

Unified project deployment & server management

Use SAS Event Stream Manager to construct and manage reusable deployment templates, easily add new ESP servers, load and unload ESP projects, and automatically scale up and down for automated elasticity and monitoring for better resource management. When ESP servers are offline or unavailable, retry capabilities support redeploying models when connectivity resumes.

Seize opportunities and spot red flags hidden in torrents of fast-moving data flowing through your business.

Use machine learning to gain insights for taking the right action.

Streaming data from operations, transactions, sensors and IoT devices is valuable – when it's well-understood. Event stream processing from SAS includes streaming data quality and analytics – and a vast array of SAS and open source machine learning and high-frequency analytics for connecting, deciphering, cleansing and understanding streaming data – in one solution. No matter how fast your data moves, how much data you have, or how many data sources you’re pulling from, it’s all under your control via a single, intuitive interface. You can define patterns and address scenarios from all aspects of your business, giving you the power to stay agile and tackle issues as they arise.

Make sound big data decisions.

Filter, normalize, categorize, aggregate, standardize and cleanse big data before you store it – saving significant staff and computing resources from having to clean up data lakes. Prebuilt data quality routines and text processing execution are applied to data in motion so big data is filtered and ready for consumption.

Scale economically from edge to enterprise for growing data volumes.

Faster, better, more powerful stream processing from edge to enterprise for high-volume throughput (millions of events per second) means low-latency response times running in distributed, in-memory grid processing commodity hardware environments. Analyze structured and unstructured data sources – including video, text, and image classification and identification – using advanced analytics with embedded AI and machine learning capabilities. In addition, SAS^® Event Stream Manager integrates SAS Event Stream Processing studio and server components, simplifying and automating the deployment of SAS Event Stream Processing projects and analytics for rapid decision making – with no disruption to service.

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FEATURED PARTNER

Cisco & SAS

Cisco and SAS are partnering in several strategic areas to help customers modernize their analytics architectures. SAS^® Analytics on the Cisco Unified Computing System^™ (UCS) integrates high-velocity delivery of information in real time. The benefits that result from conducting proofs of concepts of SAS applications with Cisco technology include reliable scalability, reduced data center costs, and improved efficiencies that allow staff to focus on strategic business initiatives.

Learn more about the Cisco & SAS partnership