streaming data

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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: 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 don’t 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: 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: 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, business technology, data science, data security
    
IBM
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 it’s 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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optimize customer service
    
Attunity
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, big data, hadoop, agile analytics, cloud data lake, cloud data warehouse, data lake ingestion, data ingestion
    
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, cloud computing, modern data integration, data integration, data analytics, cloud-based data lake, enterprise data, self-service data
    
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, database transactions, streaming environments, real-time data replication, data configuration
    
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: 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, business technology, data center, data science
    
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: 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, machine, 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: 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: AWS     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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AWS
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 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: 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, data center design and management
    
SnapLogic
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: 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 it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
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