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streaming analytics

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Published By: SAS     Published Date: Jan 17, 2018
The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence. This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
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SAS
Published By: TIBCO Software APAC     Published Date: Aug 13, 2018
The combination of legislation, market dynamics, and increasingly sophisticated risk management strategies requires you to be proactive in detecting risks like fraud quicker and more effectively. Dynamic detection systems need to adapt to evolving compliance regulations, scale to deal with growing transaction volumes, detect sophisticated risk specific patterns, and reduce false-positives. TIBCO's Risk Management Accelerator uses a combination of predictive analytics, streaming analytics, and business process management to deliver a powerful and cost-effective system for detecting anomalies. Download this solution brief to learn more.
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TIBCO Software APAC
Published By: TIBCO Software EMEA     Published Date: Sep 21, 2018
BUSINESS CHALLENGE “Vestas is a global market leader in manufacturing and servicing wind turbines,” explains Sven Jesper Knudsen, Ph.D., senior data scientist. “Turbines provide a lot of data, and we analyze that data, adapt to changing needs, and work to create a best-in-class wind energy solution that provides the lowest cost of energy. “To stay ahead, we have created huge stacks of technologies—massive amounts of data storage and technologies to transform data with analytics. That comes at a cost. It requires maintenance and highly skilled personnel, and we simply couldn’t keep up. The market had matured, and to stay ahead we needed a new platform. “If we couldn’t deliver on time, we would let users and the whole business down, and start to lose a lot of money on service. For example, if we couldn’t deliver a risk report on time, decisions would be made without actually understanding the risk landscape.
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data solution, technology solution, data science, streaming data, fast data platform, self-service analytics
    
TIBCO Software EMEA
Published By: Amazon Web Services     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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Amazon Web Services
Published By: AWS - ROI DNA     Published Date: Jun 12, 2018
Traditional data processing infrastructures—especially those that support applications—weren’t designed for our mobile, streaming, and online world. However, some organizations today are building real-time data pipelines and using machine learning to improve active operations. Learn how to make sense of every format of log data, from security to infrastructure and application monitoring, with IT Operational Analytics--enabling you to reduce operational risks and quickly adapt to changing business conditions.
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AWS - ROI DNA
Published By: SAS     Published Date: Jun 05, 2017
"The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence. This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights. "
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SAS
Published By: Amazon Web Services     Published Date: Apr 27, 2018
Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now. Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data can now be processed in memory, and more significantly, analyzed as it arrives, in real time. Millions to hundreds of millions of events (such as video streams or application alerts) can be collected and analyzed per hour to deliver insights that can be acted upon in an instant. From financial services to manufacturing, this rev
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Amazon Web Services
Published By: MarkLogic     Published Date: Nov 30, 2017
The OPDBMS market in 2017 brings cloud and fully managed options center stage for execution. Market-defining vision includes features for machine learning, serverless scenarios and streaming integration. Data and analytics leaders must balance current and future needs against this market landscape.
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MarkLogic
Published By: IBM     Published Date: Jul 01, 2015
The impact of streaming analytics and leveraging expertise in the healthcare sector.
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data insights, healthcare data, healthcare analytics, unstructured content analytics, computation-intensive analytics, ibm big data, enterprise-class data management
    
IBM
Published By: Impetus     Published Date: Mar 15, 2016
Streaming analytics platforms provide businesses a method for extracting strategic value from data-in-motion in a manner similar to how traditional analytics tools operate on data-at rest.
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impetus, guide to stream analytics, real time streaming analytics, streaming analytics, real time analytics, big data analytics, monitoring, network architecture
    
Impetus
Published By: SAS     Published Date: Jun 05, 2017
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more 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 both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
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SAS
Published By: Amazon Web Services     Published Date: May 18, 2018
We’ve become a world of instant information. We carry mobile devices that answer questions in seconds and we track our morning runs from screens on our wrists. News spreads immediately across our social feeds, and traffic alerts direct us away from road closures. As consumers, we have come to expect answers now, in real time. Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now. Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data
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Amazon Web Services
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