big data integration

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Published By: Pentaho     Published Date: Feb 26, 2015
Ventana’s benchmark research on big data integration will help you become familiar with the tools and technologies you need to help your organization succeed with big data.
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benchmark research, big data integration, big data, business intelligence systems, business analytics, data integration, research
    
Pentaho
Published By: Pentaho     Published Date: Feb 26, 2015
This TDWI Best Practices report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies.
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big data, big data analytics, data warehousing technologies, data storage, business intelligence, data integration, enterprise applications, data management
    
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: Adobe     Published Date: Nov 07, 2013
Aberdeen's Insights provide the analyst's perspective on the research as drawn from an aggregated view of research surveys, interviews, and data analysis.
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adobe, aberdeen group, analyst insight, technology tools, buying behavior, customer experience management, cem, buyer interactions, customer engagement programs, adobe customers outperform, company brand awareness, big data, structured data, data quality, data integration, customer segmentation, customer empowerment
    
Adobe
Published By: Teradata     Published Date: Jul 07, 2015
As cyber security challenges continue to grow, new threats are expanding exponentially and with greater sophistication—rendering conventional cyber security defense tactics insufficient. Today’s cyber threats require predictive, multifaceted strategies for analyzing and gaining powerful insights into solutions for mitigating, and putting an end to, the havoc they wreak.
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Teradata
Published By: IBM     Published Date: Jun 16, 2015
This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.
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cloud computing, mobility, big data, business analytics, it security landscape, enterprise networks, cloud integration, virtualization
    
IBM
Published By: IBM     Published Date: Oct 19, 2015
This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
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ibm, integration, data volume, business technology, information, it management, knowledge management, data management
    
IBM
Published By: IBM     Published Date: Jul 26, 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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scalability, data warehousing, resource planning
    
IBM
Published By: IBM     Published Date: Jul 26, 2017
Business leaders are eager to harness the power of big data. However, as the opportunity increases, ensuring that source information is trustworthy and protected becomes exponentially more difficult. If not addressed directly, end users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organizations can boost confidence in big data. A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamle
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mdm, big data, automation, organization
    
IBM
Published By: TIBCO     Published Date: Apr 08, 2013
As the volume of available data increases, and we have new ways of extracting insights from data, it is valuable to take a step back and examine the impact of these insights on integration and company actions. Join us for this webinar and learn how you can use big data in your organization.
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big data, integration, architecture, database, data warehousing, operations management
    
TIBCO
Published By: TIBCO     Published Date: Nov 11, 2013
In an IT landscape dominated by big data, mobility, social networking and cloud computing, integration will not only grow in importance, its very nature will change. This paper will discusses the 21st Century IT landscape as it relates to the new integration, and argues that the need for a comprehensive integration strategy has never been more urgent.
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tibco, mobile strategy, integration, technology, best practices, inefficient integration, platform, business strategy
    
TIBCO
Published By: IBM     Published Date: Feb 22, 2016
Survive the big data storm by getting ahead of integration and governance functional requirements
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ibm, data, big data, integration, governance, security, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage.
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ibm, data, big data, integration, hadoop, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.
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ibm, data, big data, integration, governance, iig, infosphere, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Jul 06, 2016
Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
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ibm, big data, trusted data, data management, data solutions, data center
    
IBM
Published By: IBM     Published Date: Jul 06, 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, big data, ibm infosphere, ibm biginsights, ibm bigintegrate, ibm bigquality, data management, data quality, data integration, data center
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Who's afraid of the big (data) bad wolf? Survive the big data storm by getting ahead of integration and governance functional requirements Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
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ibm, big data, trusted data, data management, data solutions, business technology, data center
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by leveraging IBM InfoSphere Information Integration and Governance (IIG) solutions.
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ibm, forrester, data, analytics, big data, ibm information integration, governance, data management, business technology, data center
    
IBM
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
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
A big data integration platform that is flexible and scalable is needed to keep up with today’s ever-increasing big data volume.
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IBM
Published By: IBM     Published Date: Apr 14, 2017
A big data integration platform that is flexible and scalable is needed to keep up with today’s ever-increasing big data volume. Download this infographic to find out how to build a strong foundation with big data integration.
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big data, big data integration, scalable data
    
IBM
Published By: IBM     Published Date: Apr 18, 2017
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes. The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
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data integration, data security, data optimization, data virtualization, database security, data analytics, data innovation
    
IBM
Published By: IBM     Published Date: Apr 18, 2017
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data. To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.
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data integration, data security, data optimization, data virtualization, database security, data migration, data assets, data delivery
    
IBM
Published By: Pentaho     Published Date: Aug 22, 2016
This white paper 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.
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big data, data integration, bi systems, hadoop
    
Pentaho
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
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