streaming data

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Published By: Attunity     Published Date: Nov 15, 2018
Change data capture (CDC) technology can modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems. To realize these benefits, enterprises need to understand how this critical technology works, why its needed, and what their Fortune 500 peers have learned from their CDC implementations. This book serves as a practical guide for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC. Read this book to understand: ? The rise of data lake, streaming and cloud platforms ? How CDC works and enables these architectures ? Case studies of leading-edge enterprises ? Planning and implementation approaches
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Attunity
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 technologiesmassive 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 couldnt keep up. The market had matured, and to stay ahead we needed a new platform. If we couldnt 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 couldnt 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: SAS     Published Date: Jan 17, 2018
The Industrial Internet of Things (IIoT) is flooding todays 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 Intels leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
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SAS
Published By: SAS     Published Date: Jan 17, 2018
Executives, managers and information workers have all come to respect the role that data management plays in the success of their organizations. But organizations dont always do a good job of communicating and encouraging better ways of managing information. In this e-book you will find easy to digest resources on the value and importance of data preparation, data governance, data integration, data quality, data federation, streaming data, and master data management.
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SAS
Published By: IBM     Published Date: Oct 19, 2015
IBM InfoSphere Information Server connects to many new at rest and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, infosphere, data profiling, business glossary, partition, pipeline parallelism, information server, data sources, hadoop, networking, security, data management
    
IBM
Published By: Cisco     Published Date: Nov 18, 2015
The Internet of Everything (IoE) is a continuous interaction among people, processes, data, and things. Sensors, networks, and smart devices are ubiquitous, providing a torrent of streaming data or big data. The Internet of Things (IoT), which is a network of physical objects accessed through the Internet that can sense and communicate, is a component of IoE. Cisco is helping customers and strategic partners leverage the full potential of IoE to achieve radical results across all sectors and industries. Indeed, IoE is capable of helping public safety and justice agencies increase cost efficiency, improve safety and security, provide better response times, and increase productivity.
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ioe, public safety, justice, emergency response, networking, security, enterprise applications
    
Cisco
Published By: IBM     Published Date: Oct 13, 2016
IBM InfoSphere Information Server connects to many new at rest and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, data, analytics, big data, data integration, data management, data center
    
IBM
Published By: SAS     Published Date: Sep 19, 2018
We are offering this second edition resource as a business oriented, working guide to core data management practices. In this ebook you will find easy to digest resources on the value and importance of data preparation, data governance, data integration, data quality, data federation, streaming data, and master data management.
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SAS
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: Ciena     Published Date: Nov 15, 2016
Research and Education (R&E) networks are experiencing a surge in capacity demand as a result of the massive growth of streaming media (Netflix, Facebook, YouTube), growing utilization of public cloud services, and the continued need to support large scientific data file transfers for researchers collaborating around the globe. This increase in traffic is driving many operators to evaluate network backbone upgrades to 100G. Upgrading is necessary but costly. But what if operators could upgrade their R&E networks to 100G for 50 percent less CAPEX investment and extend the life of the existing routers, while actually simplifying the architecture to enable lower operational costs? Download our app note to learn how.
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o-vpn, integrated access device, pinpoint, z-series, ciena 6500, ciena 5430, 6500-7, emotr, ciena 6200, ciena 5410, waveserver, wavelogic, wavelogic 3, wavelogic photonics, wavelogic3, sdh, sonet sdh, sonet/sdh, sdh network, synchronous digital hierarchy
    
Ciena
Published By: HP     Published Date: Jan 20, 2015
"Improving the operational aspects of a business can span the organizational chart, from line of business teams focused on the supply chain to IT teams reporting on communication networks and their switches. The goal is to capture the data streaming in from these various processes, and put Big Data techniques to work for you."
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big data, hp haven, scalable, secure data platform, ecosystem, security
    
HP
Published By: Amazon Web Services     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the dark data problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. 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 q
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Amazon Web Services
Published By: MemSQL     Published Date: Nov 15, 2017
THE LAMBDA ARCHITECTURE SIMPLIFIED Your Guide to Building a Scalable Data Architecture for Real-Time Workloads YOU'LL LEARN: - What defines the Lambda Architecture, broken down by each layer - How to simplify the Lambda Architecture by consolidating the speed layer and batch layer into one system - How to implement a scalable Lambda Architecture that accommodates streaming and immutable data - How companies like Comcast and Tapjoy use Lambda Architectures in production
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data, scalable, architecture, production
    
MemSQL
Published By: AWS - ROI DNA     Published Date: Jun 12, 2018
Traditional data processing infrastructuresespecially those that support applicationswerent 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 todays 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 Intels leadership in IIoT information architecture to create solutions that turn raw data into valuable insights. "
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SAS
Published By: SnapLogic     Published Date: Aug 17, 2015
This report summarizes the changes that are occurring, new and emerging patterns of data integration, as well as data integration technology that you can buy today that lives up to these new expectation
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data integration, cloud computing, mass data storage, integration requirements, integration strategies, non-persisted data streaming, device native data, data encryption, application integration, application performance management, business integration, data protection, database security, data center design and management
    
SnapLogic
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 yesterdays 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: SAS     Published Date: Jun 06, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data of many types, and from vast sources like the Internet of Things joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how its continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
Published By: Exablox     Published Date: Jan 27, 2015
As your organization grows, you need the ability to quickly and non-disruptively scale storage capacity to meet the increasing amounts of high-density graphic images, streaming media and other unstructured data. However, when using traditional storage methods, it can be complicated and expensive for you to scale capacity to meet your needs. In this second of a series of informative e-books from Exablox, we take a look at the di?iculties of traditional storage approaches, and o?er simple, practical ways to make your data storage easier to scale.
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enterprise storage, unstructured data, data mangement, data recovery, data availability, storage performance, exablox, oneblox, data management
    
Exablox
Published By: IBM     Published Date: Aug 05, 2014
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three Vs of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats. Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
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ibm, data, big data, information, healthcare, governance, technology, it management, data management
    
IBM
Published By: IBM     Published Date: Jan 13, 2016
IBM InfoSphere Information Server connects to many new at rest and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
Tags : 
ibm, data integration, infosphere, information server, partition, data profiling, knowledge management, data management, data center
    
IBM
Published By: SAS     Published Date: Aug 28, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data of many types, and from vast sources like the Internet of Things joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how its continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
Published By: Phunware     Published Date: Aug 11, 2014
Mobile devices are streaming millions of location data points in real-time. These data points are extremely valuable in their own right because the very apps that help generate data can also be used to act on insights and deliver relevant messages. Download these insights and examples to turn mobile data into actions.
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phunware, mobile, physical marketing analytics, location analytics, business intelligence, space management, customer service, crm, loyalty, analytics for mobile, analytics on mobile, app analytics, app tracking analytics, mobile analytics tool, mobile app analytics, mobile app usage analytics, mobile data analytics, mobile device analytics, mobile location analytics, knowledge management
    
Phunware
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, business activity monitoring, business analytics, analytical applications, data warehousing
    
Impetus
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