SAS Event Stream Processing

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

SAS Event Stream Processing

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

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 cloud 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.

Key Features

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

Built for speed

Enables high-volume processing of millions of events per second and low-latency response times using newly integrated ONNX Runtime for expanded support for GPU acceleration on CUDA and TensorRT supported platforms.

Ingests & consolidates streaming IoT sources

Lets you consume today's IoT sources – including cloud or edge on-site streams – and extend in the future with an extensive suite of prebuilt connectors and adapters.

Ready for real-time action

Provides you with situational awareness via customizable alerts, notifications and updates so you can react appropriately to what's happening or predicted to happen.

Flexible, open modeling environment

Easily define, test and refine models in a low code environment using an intuitive visual interface. 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.

Unified project deployment & server management

SAS Event Stream Manager lets you construct and manage reusable deployment templates, easily add new auto-discover ESP servers in cloud, load and unload ESP projects, dynamically allocate resources, 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.

Cloud native

Compatible with cloud technologies, including Docker and Kubernetes, 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, highly available event stream processing.

In-stream learning model windows

Uses 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).

Image & video analytics

Process video and still image data, including image pre-processing and object detection and classification, by combining streaming analytics, video and image ingestion with SAS and third-party machine learning frameworks using ONNX model formats.

GitHub resources

Get SAS code examples, libraries and tools for developers and users.

Solution Brief

Analyze streaming data from edge to enterprise to make intelligent decisions in real time

Learn more about how SAS Event Stream Processing helps you make real-time, intelligent decisions.

Product Brief

SAS Event Stream Processing for Edge Computing

Discover how to create new analytics-driven and AI-empowered intelligence at the smarter and more autonomous edge.

Product Brief

SAS Event Stream Manager

Find out how you can take control of your SAS Event Stream Processing environment by simplifying and automating deployments across servers and projects.

Customer Success

Look Who's Working Smarter With SAS®

Optimizing the supply chain with analytics and IoT

SAS is helping Georgia-Pacific improve equipment efficiency, reduce downtime, optimize shipping logistics and predict customer churn.

Get to Know SAS Event Stream Processing

Featured Partners

Wabtec, Intel & SAS

Wabtec, Intel and SAS are partnering to transform raw data into real-time business insight with IoT connected transportation – and unlock new levels of operational efficiency.

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