big data integration

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Published By: Pentaho     Published Date: Aug 22, 2016
This white paper covers six guidelines product leaders should explore during their embedded analytics evaluation.
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big data, data integration, bi systems, hadoop
    
Pentaho
Published By: IBM     Published Date: Oct 17, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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IBM
Published By: BMC Software     Published Date: Jul 22, 2015
Integrieren Sie Ihrer Big Data Initiativen in Ihre Unternehmensweiten Geschäftsprozesse. Gerne machen wir Sie damit vertraut, wie sie mit Control-M für Hadoop die Anwendungsentwicklung beschleunigen und die Unternehmensintegration vereinfachen können. Besprochene Themen schließen folgende Punkte ein: * Wie können Sie mit einem Enterprise Scheduler für Hadoop, weitere Automationsinseln vermeiden. * Wie kann sichergestellt werden, dass Sie aus ihren Big Data Initiativen den gewünschten Mehrwert erhalten. * Wie können Sie mit Ihren Big Data initiativen sich den administrativen Herausforderungen & Bedürfnissen stellen und mögliche Konfrontationen erfolgreich meistern.
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BMC Software
Published By: Oracle     Published Date: Feb 21, 2018
Get Started with Oracle Cloud for Free $300 in free credits Build production-ready workloads by using a variety of cloud services including databases, compute, containers, IoT, big data, API management, integration, chatbots, and many more
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Oracle
Published By: TIBCO     Published Date: Apr 29, 2014
Learn about the new IT landscape as it relates to the new integration and see why the need for a comprehensive integration strategy has never been more urgent than now.
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mobile, social, cloud, big data, tibco, comprehensive integration strategy
    
TIBCO
Published By: Pentaho     Published Date: Mar 08, 2016
If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals. Read the Buyer’s Guide to Big Data Integration by CITO Research to learn: • What tools are most useful for working with Big Data, Hadoop, and existing transactional databases • How to create an effective “data supply chain” • How to succeed with complex data on-boarding using automation for more reliable data ingestion • The best ways to connect, transport, and transform data for data exploration, analytics and compliance
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data, buyer guide, integration, technology, platform, research
    
Pentaho
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
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Amazon Web Services
Published By: Datastax     Published Date: Apr 04, 2017
As the big data ecosystem continues to expand, new technologies are addressing the requirements for managing, processing, analyzing, and storing data to help companies benefit from the rich sources of information flowing into their organizations. From NoSQL databases to open source projects to commercial products offered on-premises and in the cloud, the future of big data is being driven by innovative new approaches across the data management lifecycle. The most pressing areas include real-time data processing, interactive analysis, data integration, data governance, and security. Download this report for a better understanding of the current landscape, emerging best practices and real-world successes.
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evolution, big data, technology, datastax, nosql
    
Datastax
Published By: Enterprise Management Associates     Published Date: Aug 25, 2015
This webinar talks about various issues organization's deal with on a daily basis and how Hadoop can offer solutions.
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ema, hadoop, big data analytics, predictive insights, data lake architecture, hadoop adoption, enterprise management, business intelligence
    
Enterprise Management Associates
Published By: MoreVisibility     Published Date: Dec 19, 2017
As the approach to strategic business decision making becomes more and more data driven, a method for consolidating our various data sets, which are often spread across multiple systems becomes exceedingly important. Two of the biggest players in data driven decision making are website analytics platforms and customer relationship management systems. The former includes accumulating data on top of the funnel behavior such as site traffic origins, lead generation, content consumption tracking, device usage, and overall site behavior. While the latter has a focus more on bottom of the funnel activity such as lead nurturing, customer status, lifetime value, etc. Lastly, without communication between these two essential platforms, a complete understanding of your customers, from lead to longtime client, may never be possible. A web analytics (Google Analytics) and CRM integration provides you with a 360 degree view of your customer base, so that you can understand not just what PPC efforts
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MoreVisibility
Published By: TIBCO     Published Date: May 15, 2013
According to Forrester, most organizations today are only using 12% of their available data and only 37% of organizations are planning some type of big data technology project. At a time when companies are seeing volume of information increase quickly, it’s time to take a step back and look at the impact of big data. Join Mike Gualtieri, Principal Analyst at Forrester, for this webcast exploring the importance of integration in your big data initiatives. Discover how your ability to operate, make decisions, reduce risks and serve customers is inextricably linked to how well you’re able to handle your big data. Continue on to gain insight into: •3 key big data management activities you need to consider •Technologies you need to create for your big data ecosystem •A multi-dimensional view of the customer is the holy grail of individualization •Overcoming key integration challenges And more!
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big data, integration, architecture, database, data warehousing, operations management
    
TIBCO
Published By: TIBCO     Published Date: May 15, 2013
•Big Data + Integration •Integration In the Age of The Customer •Impact of Mobile
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big data, architecture, mobile, integration, customer, webinar
    
TIBCO
Published By: TIBCO     Published Date: Aug 05, 2014
This ebook explains how the TIBCO Fast Data architecture delivers the right information—and the right decision—at the right place and time.
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tibco, tibco software, big data, business intelligence, esb, enterprise service bus, soa, application integration
    
TIBCO
Published By: SnowFlake     Published Date: Jul 08, 2016
In the era of big data, enterprise data warehouse (EDW) technology continues to evolve as vendors focus on innovation and advanced features around in-memory, compression, security, and tighter integration with Hadoop, NoSQL, and cloud. Forrester identified the 10 most significant EDW software and services providers — Actian, Amazon Web Services (AWS), Hewlett Packard Enterprise (HPE), IBM, Microsoft, Oracle, Pivotal Software, SAP, Snowflake Computing, and Teradata — in the category and researched, analyzed, and scored them. This report details our findings about how well each vendor fulfills our criteria and where they stand in relation to each other to help enterprise architect professionals select the right solution to support their data warehouse platform.
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forrester, enterprise, data, technology, best practices, innovation, security
    
SnowFlake
Published By: IBM     Published Date: May 27, 2014
Big data and analytics help insurance companies identify the next best action for customers. With the right solutions, companies can extract, integrate and analyze a large volume and variety of data, from call-center notes and voice recordings to web chats, telematics and social media
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ibm, big data, analytics, insurance, insurance industry, big data solutions, integration, risk assessment
    
IBM
Published By: IBM     Published Date: May 27, 2014
If insurers want to succeed in today's digital world, they need to create experiences and business models that are orchestrated, symbiotic, contextual and cognitive.
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ibm, big data, insurance, digital insurer, business model, insurance industry, technology, integration
    
IBM
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
Published By: Pentaho     Published Date: Jan 16, 2015
Download if you need to make decisions about the architecture of the systems you work on. Sponsored by Pentaho.
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embedded, big data, nosql, hadoop, customer analytics, data integration, data delivery
    
Pentaho
Published By: IBM     Published Date: May 28, 2014
Read the whitepaper to find out how one client improved business value of their data by implementing InfoSphere Optim processes and technologies.
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ibm, data lifecycle management, infosphere optim, integrating big data, governing big data, integration, best practices, big data
    
IBM
Published By: IBM     Published Date: May 28, 2014
The right test data management solution accelerates time to value for business-critical applications and builds relationships and efficiencies across the organization. IBM InfoSphere Optim Test Data Management closes the gap between DBAs and application developers by providing all teams with accurate, appropriately masked and protected data for their work. Developers can confirm that new application functionalities perform as expected. QA staff can validate that the application performs as intended based on the test cases, and that integrations work properly. And business leaders can be more confident that competitive functionality will be delivered on time with less risk.
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ibm, ibm infosphere, ibm optim solutions, ibm test data, test data management, dba, database management, big data
    
IBM
Published By: IBM     Published Date: May 28, 2014
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
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ibm, data retention, information governance, archiving, historical data, integrating big data, governing big data, integration
    
IBM
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 V’s 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
    
IBM
Published By: IBM     Published Date: Oct 01, 2014
This white paper discusses how IBM InfoSphere can support the integration and governance of Big Data in healthcare. The white paper reviews three case studies including predictive analytics with Electronic Medical Records, time series data in a neonatal intensive care unit and predictive pathways for disease.
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big data, data integration, healthcare
    
IBM
Published By: IBM     Published Date: Nov 06, 2014
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
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big data, data integration, analytics, customer matching, ibm
    
IBM
Published By: IBM     Published Date: Jan 14, 2016
Built using the IBM® InfoSphere® Information Server, IBM BigInsights® BigIntegrate and BigInsights BigQuality provide the end-to-end information integration and governance capabilities that organizations need.
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ibm, biginsights, bigintegrate, bigquality, data, ecosystem, governance, integration
    
IBM
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