streaming analytics

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Published By: TIBCO Software     Published Date: Jul 22, 2019
The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and the need to rework faulty products. To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support. At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
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TIBCO Software
Published By: TIBCO Software     Published Date: Jul 22, 2019
Global producer of polycrystalline silicon for semiconductors, Hemlock Semiconductor needed to accelerate process optimization and eliminate cost. With TIBCO® Connected Intelligence, Hemlock achieved centralized, self-service, governed analysis; revenue gains; cost savings; and more. Fueled by double-digit growth in the markets it serves, Hemlock Semiconductor is adapting to the increasing commoditization within the polysilicon industry and better positioning itself to compete. A key factor in this plan is to equip process-knowledgeable personnel with the skills and tools to accelerate delivery of process optimizations and associated cost elimination. Hemlock turned to a TIBCO® Connected Intelligence solution to address the challenges. By implementing TIBCO Spotfire® and TIBCO® Streaming analytics, TIBCO® Data Science, and TIBCO® Data Virtualization, the company created more self-service analytics. Adding TIBCO BusinessWorks™ integration let the company realize the vision of connect
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TIBCO Software
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 GmbH     Published Date: Jan 15, 2019
The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and no need to rework faulty products. To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support. At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
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TIBCO Software GmbH
Published By: Attunity     Published Date: Jan 14, 2019
This whitepaper explores how to automate your data lake pipeline to address common challenges including how to prevent data lakes from devolving into useless data swamps and how to deliver analytics-ready data via automation. Read Increase Data Lake ROI with Streaming Data Pipelines to learn about: • Common data lake origins and challenges including integrating diverse data from multiple data source platforms, including lakes on premises and in the cloud. • Delivering real-time integration, with change data capture (CDC) technology that integrates live transactions with the data lake. • Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assemble a historical data store. • Leveraging a scalable and autonomous streaming data pipeline to deliver analytics-ready data sets for better business insights. Read this Attunity whitepaper now to get ahead on your data lake strategy in 2019.
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data lake, data pipeline, change data capture, data swamp, hybrid data integration, data ingestion, streaming data, real-time data
    
Attunity
Published By: Attunity     Published Date: Feb 12, 2019
This technical whitepaper by Radiant Advisors covers key findings from their work with a network of Fortune 1000 companies and clients from various industries. It assesses the major trends and tips to gain access to and optimize data streaming for more valuable insights. Read this report to learn from real-world successes in modern data integration, and better understand how to maximize the use of streaming data. You will also learn about the value of populating a cloud data lake with streaming operational data, leveraging database replication, automation and other key modern data integration techniques. Download this whitepaper today for about the latest approaches on modern data integration and streaming data technologies.
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streaming data, cloud data lakes, cloud data lake, data lake, cloud, data lakes, streaming data, change data capture
    
Attunity
Published By: Attunity     Published Date: Feb 12, 2019
Read this technical whitepaper to learn how data architects and DBAs can avoid the struggle of complex scripting for Kafka in modern data environments. You’ll also gain tips on how to avoid the time-consuming hassle of manually configuring data producers and data type conversions. Specifically, this paper will guide you on how to overcome these challenges by leveraging innovative technology such as Attunity Replicate. The solution can easily integrate source metadata and schema changes for automated configuration real-time data feeds and best practices.
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data streaming, kafka, metadata integration, metadata, data streaming, apache kafka, data integration, data analytics
    
Attunity
Published By: Intel     Published Date: Dec 13, 2018
In today’s world, advanced vision technologies is shaping the next era of Internet of Things. However, gathering streaming video data is insufficient. It needs to be timely and accessible in near-real time, analyzed, indexed, classified and searchable to inform strategy—while remaining cost-effective. Smart cities and manufacturing are prime examples where complexities and opportunities have been enabled by vision, IoT and AI solutions through automatic meter reading (AMR), image classification and segmentation, automated optical inspection (AOI), defect classification, traffic management solution—just to name a few. Together, ADLINK, Touch Cloud, and Intel provide a turnkey AI engine to assist in data analytics, detection, classification, and prediction for a wide range of use cases across a broad spectrum of sectors. Learn more about how the Touch Cloud AI brings cost savings, operational efficiency and a more reliable, actionable intelligence at the edge with transformative insi
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Intel
Published By: TIBCO Software     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
Published By: TIBCO Software     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
Published By: TIBCO Software     Published Date: Feb 01, 2019
The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and no need to rework faulty products. To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support. At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
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data, product, manufacturers, manufacturing, processes, technologies, analysis, quality
    
TIBCO Software
Published By: TIBCO Software     Published Date: Aug 20, 2019
Endesa is a leading energy company in Spain and Portugal with around 10,000 employees, providing services for over 11 million customers. The company is committed to spreading a more sustainable energy culture and strives to be at the forefront of the technological transformation of the energy industry. To meet this goal, Endesa joined the Enel Group in 2009, a multinational energy company and leading integrated operator in the global electricity and gas markets, with a particular focus on European and Latin American markets.
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endesa, endel group, open power concept, spotfire, streaming analytics
    
TIBCO Software
Published By: AWS     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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AWS
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: AWS     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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AWS
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, information technology
    
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
    
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: AWS     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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