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.
Customer Success
Look Who's Working Smarter With SAS
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Customer Story
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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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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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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.
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Customer Story
End-to-end integrated management of marketing activitiesTunisie Telecom is focused on consolidating Tunisia’s telecoms infrastructure and improving network coverage in the country. The organisation actively contributes to the promotion of ICT and the development of innovative companies in the field of telecoms.
Tunisie Telecoms is considered a pioneer of the telecoms sector in Tunisia and focuses on a customer-centric approach. It has more than six million subscribers in fixed and mobile services, 24 regional departments, 140 TT spaces, and more than 13 000 private points of sales. It employs in excess of 6 000 agents.
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Customer Story
Gestion de bout en bout des activités marketing de façon intégréeLa solution proposée par notre partenaire SAS, est venue répondre à des problématiques de gestion quotidienne de notre clientèle. Il s’agit en l’occurrence de la digitalisation de nos process, du reporting et de la gestion des réclamations des abonnés. Grâce aux solutions SAS, nous parvenons maintenant à lancer plusieurs campagnes de promotion pour nos clients. Désormais, nous avons plus de visibilité sur les résultats de ces dernières en temps réel. Le déploiement de la solution CVM de SAS nous a aussi permis de redéployer nos ressources humaines à d’autres missions à forte valeur ajoutée, notamment l’analyse des données générées et la mise en place de la stratégie commerciale du groupe.
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White Paper
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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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Understanding data in motionLearn how to analyze fast-moving data streams on the fly with event stream processing.
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