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data warehouses

Results 26 - 47 of 47Sort Results By: Published Date | Title | Company Name
By: Oracle     Published Date: Apr 16, 2018
A velocidade e o volume de entrada de dados estão gerando demandas esmagadoras sobre os data marts tradicionais, os data warehouses e os sistemas analíticos. Uma solução em nuvem de data warehouse tradicional pode ajudar os clientes a suprirem tais demandas? Muitos clientes estão comprovando o valor dos data warehouses na nuvem através dos ambientes de testes ou de inovação, dos data marts na área de negócios e backup de banco de dados.
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clientes, estao, migrando, data, warehouses, nuvem
    
Oracle
By: Oracle     Published Date: Apr 16, 2018
La velocidad y el volumen de los datos entrantes están dando lugar a una gran demanda en los centros de datos tradicionales, repositorios de datos empresariales y sistemas analíticos. ¿Puede una solución de almacén de datos tradicional en la nube ayudar a los clientes a satisfacer estas demandas? Muchos clientes están comprobando el valor de los repositorios de datos en la nube a través de entornos “de prueba”, repositorios de datos según el área de negocios y respaldos de base de datos.
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clientes, trasladan, sus, data, warehouses
    
Oracle
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
By: SRC,LLC     Published Date: Jun 01, 2009
Companies spend millions of dollars every year on building data warehouses, buying business intelligence (BI) software tools and managing their analytic processes in the hope of gaining consumer insight and winning market share. Yet, many companies fail to realize the full benefits of their technology investments because they are hamstrung by the layers of expertise and the complexity of technology tools needed to integrate various data warehouses and associated tools within their existing analytic environments. Since analysis is only as good as the accessibility, timeliness and accuracy of the information being analyzed, the interoperability of any data warehouse with any analytic environment is essential to achieving insightful, actionable analysis and making better decisions.
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src, enterprise, streamline, analytics, economy, analytic imperative, business intelligence, seamless
    
SRC,LLC
By: DataFlux     Published Date: Jan 07, 2011
This white paper introduces and examines a breakthrough platform solution designed to drive parallel-process data integration - without intensive pre-configuration - and support full-lifecycle data management from discovery to retirement.
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dataflux, enterprise data, data integration, configuration, lifecycle data management, data warehouses, enterprise software, product lifecycle management
    
DataFlux
By: Oracle Corporation     Published Date: May 11, 2012
This white paper presents two case studies that illustrate how Oracle Exadata increased storage capacity for data warehouses by 150%, reduced operational and database running costs by 50%, and on average improved database query performance by 10x.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs
    
Oracle Corporation
By: AWS - ROI DNA     Published Date: Jun 12, 2018
Traditional databases and data warehouses are evolving to capture new data types and spread their capabilities in a hybrid cloud architecture, allowing business users to get the same results regardless of where the data resides. The details of the underlying infrastructure become invisible. Self-managing data lakes automate the provisioning, reliability, performance and cost, enabling data access and experimentation.
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AWS - ROI DNA
By: Vertica     Published Date: Mar 15, 2010
In a world of growing data volumes and shrinking IT budgets, it is critical to think differently about the efficiency of your database and storage infrastructure. The Vertica Analytic Database is a high-performance, scalable and cost-effective solution that can bring dramatic savings in hardware, storage and operational costs.
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vertica, ec2, cdr, elastic, saas, cdr, cloud computing, data management
    
Vertica
By: IBM     Published Date: Dec 30, 2008
Most long-standing data warehouses are designed to support a relatively small number of users who access information to support strategic decisions, financial planning and the production of standard reports that track performance. Today, many more users need to access information in context and on demand so that critical functions are optimized to run efficiently. Learn how to create a roadmap for a truly dynamic warehousing infrastructure, and move ahead of your competition with your business intelligence system
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warehousing infrastructure, ibm, business intelligence, data warehouse, dynamic warehousing, data warehouse model, master data, data management
    
IBM
By: IBM     Published Date: Feb 02, 2009
A comprehensive solution for leveraging data in today's financial industry. Most organizations realize that the key to success lies in how well they manage data—and the banking industry is no exception. From customer statistics to strategic plans to employee communications, financial institutions are constantly juggling endless types of information.
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ibm, information management software, leveraging data, dynamic warehousing, data management, improve customer service, real-time risk analysis, analytics capabilities
    
IBM
By: IBM     Published Date: Jun 15, 2009
The ability to make quick, well-informed decisions is critical to competitiveness and growth for most companies. Read the white paper to see how Data warehouse solutions can deliver business insight across virtually any business process or function. And also how they're particularly valuable for understanding sales, profiling customers and analyzing business costs.
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ibm, data warehouses, warehouse, data, data solutions, sales, business costs, olap
    
IBM
By: SnowFlake     Published Date: Jul 08, 2016
Data today comes from diverse sources in diverse forms and needs to be analyzed by ever more users as quickly as possible. Those demands are stressing the limitations of traditional data warehouses and data platforms. Snowflake has reinvented the data warehouse, making it possible to bring all your business data together in a single system that can support all your users and workloads. Built from the cloud up as a software service, Snowflake eliminates the cost, complexity, and inflexibility of existing solutions while allowing you to use the tools and skills you already have.
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snowflake, data, technology, best practices, solutions, cloud support, storage, business intelligence
    
SnowFlake
By: SnowFlake     Published Date: Jul 08, 2016
This EMA case study profiles the implementation of the Snowflake Elastic Data Warehouse, a new generation of cloud-based data warehouses, by Accordant Media. This document details significant tangible and intangible improvements and opportunities the Snowflake solution created for the Accordant Media infrastructure and analytical teams.
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snowflake, media, data, technology, cloud-based data, best practices, business intelligence, productivity
    
SnowFlake
By: SAS     Published Date: Sep 08, 2010
This paper describes five business analytics styles used today and the building blocks required in implementing these styles. It is important to consider which of these styles is valid for your organization now and into the future.
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sas, reporting, data warehouses, business activity monitoring, data integration, business analytics, service oriented architecture, data warehousing
    
SAS
By: IBM     Published Date: Nov 16, 2015
The market offers an array of choices to organizations planning new data warehouses to manage large and varied data sets. Most vendors emphasize the speed of their products, but few address the real need: to increase speed efficiently, which reduces complexity and cost by simplifying data warehousing.
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ibm, organization, puredata, analytics, research, data warehouse, knowledge management, data management
    
IBM
By: IBM     Published Date: Mar 05, 2014
For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years allowing organizations to use business intelligence (BI) tools to analyze, compare and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.
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ibm, big data, data, big data platform, analytics, data sources, data complexity, data volume
    
IBM
By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache, networking
    
Altiscale
By: IBM     Published Date: Oct 06, 2014
Business Intelligence (BI) has become a mandatory part of every enterprise’s decision-making fabric. Unfortunately in many cases, with this rise in popularity, came a significant and disturbing complexity. Many BI environments began to have a myriad of moving parts: data warehouses and data marts deployed on multiple platforms and technologies – each requiring significant effort to ensure performance and support for the various needs and skill sets of the business resources using the environment. These convoluted systems became hard to manage or enhance with new requirements. To remain viable and sustainable, they must be simplified. Fortunately today, we have the ability to build simpler BI technical environments that still support the necessary business requirements but without the ensuing management complexity. This paper covers what is needed to simplify BI environments and the technologies that support this simplification.
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data warehouses, bi environments, bi technologies, faster deployments, knowledge management, business analytics, business management, business process management
    
IBM
By: IBM     Published Date: Jan 14, 2015
Decision makers need data and they need it now. As the pace of business continues to accelerate, organizations are leaning heavily on data warehouses to deliver analytical grist for the mill of daily decisions. This Research Report from Aberdeen Group examines the benefits of data warehouse solutions that offer rapid information delivery while minimizing complexity for users and IT.
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aberdeen group, data warehouse, data center, data management, analytic tools, collaboration, data trust, data analytics
    
IBM
By: Safe Software     Published Date: Aug 21, 2009
Spatial data warehouses are becoming more common as government agencies, municipalities, utilities, telcos and other spatial data users start to share their data. This paper illustrates some of the issues that arise when undertaking data replication and data sharing.
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data warehousing, share data, data management, data distribution, data sharing, replication, safe, safe software
    
Safe Software
By: WorldTelemetry, Inc.     Published Date: Mar 26, 2007
Business Intelligence Software are applications that build on existing data warehouses and provide analytical processing tools that allow users to more effectively analyze such data. This, in turn, permits businesses to more rapidly develop existing and new analyses and reports for improved decision-making power and information dissemination capacity.
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analytical applications, business analytics, business metrics, business intelligence, enterprise software, bi software, world telemetry, worldtelemetry
    
WorldTelemetry, Inc.
By: IBM     Published Date: Mar 30, 2017
In today’s competitive on-line world, the speed of change in customer behaviour is increasing. In addition, in industries such as retail banking, car insurance and to some extent retail, the Internet has become the dominant way in which customers interact with an organisation. Yet in many data warehouses today, being able to analyse customer on-line behaviour is often not possible because the clickstream web log data needed to do this is missing. It is a key point because customer access to the web has made loyalty cheap.
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cyber attacks, data protection, it security, security solutions, system protector, web security, analytics
    
IBM
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