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 on GPU, CPU and other commodity hardware.
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.
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 powerful neural networks.
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).
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.
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.
CLOUD PROVIDERS
Conquer all your analytics challenges – from experimental to mission critical – with faster decisions in the cloud. The latest release of SAS Viya is now available on these cloud providers.
Explore More on SAS® Event Stream Processing and Beyond
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Article
The opportunity of smart grid analyticsWith smart grid analytics, utility companies can control operating costs, improve grid reliability and deliver personalized energy services.
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Article
Understanding data in motionLearn how to analyze fast-moving data streams on the fly with event stream processing.
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Article
Making sense of streaming data in the Internet of ThingsThe Internet of Things has changed our lives forever. Read about how streaming data in IoT works, and why it has caused such a shift in the analytics world.
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White Paper
The Evolution of AnalyticsLearn about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
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Article
Three C’s of the connected customer in the IoTTo optimize the connected customer experience, Blue Hill Research says organizations should build an IoT model based on three key features.
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Analyst Report
Vendor Rating: SASSAS leads in several markets where it leverages its broad product portfolio, expertise in advanced analytics and partner ecosystem. SAS has been aggressive in driving AI and machine learning innovation in its portfolio. SAS Viya platform is a critical change that is cloud-ready and leverages open-source software.
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Analyst Report
Gartner positions SAS as a leader in the Magic Quadrant for Data Integration ToolsSAS is a leader in Gartner's Magic Quadrant for Data Integration Tools. Data integration from SAS helps you break down data silos and boost productivity.
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Customer Story
IoT data with artificial intelligence reduces downtime, helps truckers keep on truckingVolvo Trucks and Mack Trucks use sensor data and SAS AI solutions to minimize unplanned downtime.
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Customer Story
Better customer service via machine learningAdvanced analytics helps Rogers Communications become more customer-centric, cutting customer complaints in half.
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Article
IoT in health care: Unlocking true, value-based careGiven the potential of IoT – and the challenges of already overburdened health care systems around the world – we can’t afford not to integrate IoT in health care.
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E-Book
Streaming analytics: Life happens fast. Are your analytics keeping up?Download this free e-book to find out what streaming analytics is, the unique opportunities it can address and five key success factors for planning your streaming analytics program.
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White Paper
Using Hybrid Cloud Capabilities for True Omnichannel MarketingSeamless, agile customer interactions require a marketing system that can collect data about a customer’s interactions and behavior across all touch points, regardless of underlying technology. Learn how SAS Customer Intelligence 360 lets you use both cloud and on-site channels and data to create an omnichannel marketing solution.
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White Paper
Understanding Data Streams in IoTThis paper explains how streaming analytics helps you acquire, understand and use real-time, streaming data to make fact-based, automated decisions – and instantaneously react to new information.
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Article
5 ways to measure beehive health with analytics and hive-streaming dataThis analytical approach to understanding bee hive health can automatically alert beekeepers to changes in hive weights, temperatures, flight activity and more.
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White Paper
Data Analytics at the Edge451 Research explores the value of diverse IoT data that is processed and analyzed at the edge in environments like T&D facilities, oil fields and manufacturing shop floors.
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White Paper
The Artificial Intelligence of Things We’re living in a world that has more connected devices than humans. See how AI amplifies the value and potential of this fast-growing Internet of Things.
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SAS® Analytics for IoTDrive innovation, efficiencies and results by putting powerful IoT analytics with embedded AI and industry-leading streaming capabilities in users' hands.
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SAS® Intelligent DecisioningEnable analytically driven real-time interactions, and automate operational business decisions at scale.
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SAS® Model ManagerRegister, modify, track, score, publish and report on analytical models through a web interface that is integrated with the model building process.
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SAS® Visual AnalyticsVisually explore all data, discover new patterns and publish reports to the web and mobile devices.
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SAS® Viya®Conquer your analytics challenges, from experimental to mission critical, with faster decisions in the cloud. SAS Viya enables everyone – data scientists, business analysts, developers and executives alike – to collaborate, scale and operationalize insights, everywhere.
