Event streaming analytics
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 Packs the Most Analytics Punch
“SAS Event Stream Processing (ESP) stands out as the platform with the most built-in analytics for machine learning and other advanced analytics, as well as a mature edge analytics capability for IoT applications.”
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.
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.
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.
Get SAS code examples, libraries and tools for developers and users.
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
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.
Disponible sur votre fournisseur de cloud préféré
Relevez tous vos défis avec l'analytique en prenant des décisions plus rapides dans le Cloud.
Explore More on SAS Event Stream Processing and Beyond
To browse resources by type, select an option below.
- Select resource type
- Analyst Report
- Blog Post
- Book Excerpt
- Case Study
- Customer Story
- Partner Brief
- Product Brief
- Solution Brief
- Séminaire web
- White Paper
- White Paper
- Customer Story Managing Dutch roads and waterways with intelligenceA modern AI, IoT and analytics platform powered by SAS Viya helps Rijkswaterstaat move from reactive to predictive infrastructure maintenance.
- Article The opportunity of smart grid analyticsWith smart grid analytics, utility companies can control operating costs, improve grid reliability and deliver personalized energy services.
- 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.
- Customer Story Transforming steelmaking through IoT analyticsSSAB improves production efficiency, product quality and maintenance strategies using sensor data, artificial intelligence and advanced analytics.
- Customer Story 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.
- 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.
- 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.
- Article Understanding data in motionLearn how to analyze fast-moving data streams on the fly with event stream processing.
- White Paper Channeling Streaming Data for Competitive AdvantageDiscover how and why innovative companies are transforming business operations by using streaming analytics to extract meaning from live data streams as data is created, and automate reactions to it with millisecond response times.
- Article Components of an information management strategyBefore starting a data management strategy for your business, you need to understand each component. Data expert David Loshin breaks them down.
- White Paper Drive Next-Generation Performance With Real-Time Connected Manufacturing This special report sponsored by SAS, Intel and Hortonworks explores how manufacturers are capitalizing on the promise of the IoT, creating new value streams and revolutionizing the relationship with markets.
- White Paper Drive Next-Generation Customer Experiences with Real-Time Connected Product and Service Analytics SAS, Hortonworks and Intel can help you embrace technologies and processes to anticipate a wide range of customer needs, providing the foundation for next-generation customer experiences.
- 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.
- 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.
- White Paper The Autonomous Grid in the Age of the Artificial Intelligence of ThingsExplore how AI and IoT work together to deliver everything from improved threat detection to better customer engagement for utilities.
- White Paper TDWI Checklist Report Six Best Practices to Ignite the Customer Experience with IoTcustomer data, IoT
Check out these products and solutions related to SAS Event Stream Processing.
- Easily create, manage and govern robust, analytically driven business rules to power decisioning at scale.
- Register, modify, track, score, publish and report on analytical models through a web interface that is integrated with the model building process.
- Découvrez et explorez les relations entre les données et partagez vos observations.