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.
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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.
- Analyst Report IDC Spotlight: Looking Beyond the AI Model to Improve the Value of Computer Vision in Manufacturing OperationsThis IDC Spotlight explores the increasing adoption of computer vision (CV) by manufacturers, and how in order to succeed, they must focus on more than just model development, validation and deployment. SAS delivers a comprehensive portfolio of tools, software and resources – integrated into SAS Viya – to develop and deploy CV solutions.
- 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 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.
- 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
- 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.
- White Paper Securing Your IoT Solution StackLearn why IoT solutions are so difficult to secure, what's needed to secure each layer of the IoT stack, and how SAS uses software like SAS Event Stream Processing to secure the applications layer.
- White Paper How Streaming Data Analytics Enables Real-Time DecisionsLearn how SAS provides extremely high-throughput, low-latency technology to meet whatever streaming analytics your company might want to pursue.
- White Paper Analytics Accelerates Monetization Opportunities for Connected Vehicle and Mobility ServicesLearn how automakers and their partners are using IoT data and analytics to help them reshape business models, seize new sources of revenue and develop inventive ways to better serve customers.
- 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 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.
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