warehouse

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Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
This report explores a new breed of data warehouse that can operate in a world of legacy on-premise systems while exploiting the potential of cutting edge technologies and deployment styles
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
In this paper, we focus on the DWA and how it has evolved over the years since its introduction. The XDW architecture is then described, in which the need to maintain the data warehouse is documented while adding new components and capabilities to extend the analytical capabilities. This section also discusses the appropriate usage of appliances within the XDW. The rest of the paper covers the benefits from implementing the DWA, the selection considerations for them and what the future holds for them.
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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
As the foundation for most critical business decisions, today's data environments are not just a vital piece of IT infrastructure, but key component of corporate strategy.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
One of the biggest changes faces organizations making purchasing and deployment decisions about analytic databases -- including relational data warehouses -- is whether to opt for a cloud solution.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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Group M_IBM Q2'19
Published By: Canon Business Process Services, Inc.     Published Date: Aug 17, 2015
Every business day, corporate campuses across the country function like small cities requiring highly specialized facilities and logistics management services. This paper will explore effective and efficient Corporate Campus Logistics Services to ensure the efficient flow of materials and people at large multi-building office campuses serving thousands of employees. Corporate facilities, real estate management, logistics and procurement teams as well as commercial real estate management companies are charged with sourcing the services that are the underpinnings of these mega-campuses. It’s an effort that spans a wide range—coordinating the receipt and distribution of supplies, equipment and packages; safely transporting thousands of employees as well as suppliers and contractors to locations within the campus; tracking and warehousing countless materials to support daily planned and unplanned workplace needs. Learn more about lowering costs, streamlining operational efficiency and gaining high employee satisfaction.
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canon business process services, outsourcing services, managed services, logistics services, warehouse management, facilities management, commercial real estate management, corporate real estate management, business operations, vendor management, performance management, service level agreements, slas, knowledge management
    
Canon Business Process Services, Inc.
Published By: Epicor     Published Date: Aug 02, 2012
The capabilities that enterprise resource planning (ERP) systems enable in wholesale and distribution have a wide umbrella of influence throughout an organization. Using ERP data to make better business decisions while cutting your costs is only one way you can make the most of your system.
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wholesale distribution, leading wholesalers, leading distributors, erp strategies, erp capabilities, cross functional teams, full visibility, operations and transactions, technology aids, erp modules and extensions, forecasting, demand planning, transportation management, warehouse management, supply chain planning, supplier relationship management, distribution requirements planning, optimization, measurable benefits, social media
    
Epicor
Published By: Red Hat, Inc.     Published Date: Jul 10, 2012
Is data changing the way you do business?Is it inventory sitting in your warehouse? The good news is data-driven applications enhance online customer experiences, leading to higher customer satisfaction and retention, and increased purchasing.
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it planning, data, data-driven applications, data challenges, data solutions, big data solutions, big data challenges, in-memory databases, web-abpplications, in-memory data grid, nosql, storage nodes, e-commerce applications, social applications, logisitcs applications, trading applications, data scaling, rest, memcached, hot rod
    
Red Hat, Inc.
Published By: SAS     Published Date: Nov 10, 2014
Learn how data is evolving and the 7 reasons why a comprehensive data management platform supersedes the data integration toolbox that you are using these days.
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sas, data integration, data evolution, comprehensive data, data management, data virtualization, data warehouses, data profiling, metadata management, best practices, business activity monitoring, data center
    
SAS
Published By: SAS     Published Date: Nov 10, 2014
Learn how this upcoming year should be the year you make your big data actionable and see what else you should be doing to maximize its potential.
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sas, data integration, data evolution, comprehensive data, data management, data virtualization, data warehouses, data profiling, metadata management, best practices, business intelligence, data center
    
SAS
Published By: OpTier     Published Date: Mar 11, 2013
In the Information Technology (IT) industry, 2012 has been the year of Big Data. From a standing start toward the end of the last decade, Big Data has become one of the most talked about topics.
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optier, big data, enterprise data warehouse, edw, nosql
    
OpTier
Published By: Oracle     Published Date: Nov 06, 2012
The purpose of this white paper is to take a time-to-business-value look at financial services data warehousing technologies with a focus on the selection process and how it should take deeper considerations of the real-world implementation hurdles.
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oracle, data, analytical data, data marts, industry-specific data warehouse, financial services
    
Oracle
Published By: Oracle     Published Date: Nov 06, 2012
The purpose of this white paper is to take a time-to-business-value look at financial services data warehousing technologies with a focus on the selection process and how it should take deeper considerations of the real-world implementation hurdles.
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oracle, data, analytical data, data marts, industry-specific data warehouse, financial services
    
Oracle
Published By: Epicor     Published Date: Jul 06, 2017
Distribution is being transformed by digital innovations. More and more distributors are replacing manual, paper-driven processes with digital tools and automation that allow employees to work more efficiently and effectively in the warehouse and beyond. Efficiency doesn’t just benefit employees—modern technology can help keep customers and vendors happier with up-to-date information accessible anytime. Epicor Prophet 21 software was designed to meet the unique needs of distributors. It offers robust features including eCommerce, mobility tools, customer relationship management (CRM), wireless warehouse management, purchase management, and analytics to make more informed business decisions. With deep industry knowledge and best practices built into features throughout the solution, Prophet 21 can deliver the digital transformation your organization needs to grow.
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erp software, enterprise resource planning software, distribution software, prophet 21, p21, inventory management
    
Epicor
Published By: Epicor     Published Date: Jan 16, 2018
By now, mobile technology has become an essential part of people’s lives. As both consumers and staff trend more toward a younger, digitally savvy demographic, lumber and building materials (LBM) businesses need to take advantage of mobile tools or risk losing to the competition. Mobile technologies can bring incredible benefits to LBM enterprises for delivery and dispatch, field sales, the selling floor, and the warehouse. To better help you seize the mobile advantage, Epicor has identified eight ways your LBM business can leverage mobile technologies to foster growth, including: • Serving your customers with the latest information • Serving your customers with timely, accurate deliveries • Driving revenue with increased efficiency • Streamlining operations Check out this Epicor tipsheet and discover how else your LBM business can benefit from mobile technologies.
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lumber, building materials, lbm, erp, bistrack, lumber distributors
    
Epicor
Published By: Epicor     Published Date: Nov 19, 2018
By now, mobile technology has become an essential part of people’s lives. As both consumers and staff trend more toward a younger, digitally savvy demographic, lumber and building materials (LBM) businesses need to take advantage of mobile tools or risk losing to the competition. Mobile technologies can bring incredible benefits to LBM enterprises for delivery and dispatch, field sales, the selling floor, and the warehouse. To better help you seize the mobile advantage, Epicor has identified eight ways your LBM business can leverage mobile technologies to foster growth, including: • Serving your customers with the latest information • Serving your customers with timely, accurate deliveries • Driving revenue with increased efficiency • Streamlining operations Read out this Epicor eBook and discover how else your LBM business can benefit from mobile technologies.
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lumber, building materials, lbm, erp, bistrack, lumber distributors
    
Epicor
Published By: Hortonworks     Published Date: Apr 05, 2016
Download this whitepaper to learn how Hortonworks Data Platform (HDP), built on Apache Hadoop, offers the ability to capture all structured and emerging types of data, keep it longer, and apply traditional and new analytic engines to drive business value, all in an economically feasible fashion. In particular, organizations are breathing new life into enterprise data warehouse (EDW)-centric data architectures by integrating HDP to take advantage of its capabilities and economics.
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Hortonworks
Published By: Zebra Technologies     Published Date: Jun 21, 2017
Redefining supply chain automation in the age of digital technology. Download now to learn more!
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Zebra Technologies
Published By: Zebra Technologies     Published Date: Jun 21, 2017
Best practices for integrating mobile, wireless and data capture technologies into warehouse management. Download now!
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Zebra Technologies
Published By: Sage     Published Date: Jul 08, 2015
Download this research survey to learn more about the returns of technology investments for small and midsize companies.
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technology investments, small company, midsize company, efficiency, technology integration, mobile technology, warehouse logistics, small business
    
Sage
Published By: Oracle     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle
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