data warehouse

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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
Best practices for integrating mobile, wireless and data capture technologies into warehouse management. Download now!
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Zebra Technologies
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
Published By: SAP Inc.     Published Date: Jul 28, 2009
Although many organizations have made significant investments in data collection and integration (through data warehouses and the like), it is a rare enterprise that can analyze and redeploy its accumulated data to actually drive business performance.  In the years to come, as globalization and increased reliance on the Internet further complicate, accelerate and intensify marketplace conditions, actionable business intelligence promises to deliver a formidable competitive advantage to firms that leverage its power.
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sap, business intelligence, business insight, business transparency, cross-enterprise data, inter-enterprise data, data integration, enterprise applications, data management
    
SAP Inc.
Published By: Pentaho     Published Date: Apr 28, 2016
As data warehouses (DWs) and requirements for them continue to evolve, having a strategy to catch up and continuously modernize DWs is vital. DWs continue to be relevant, since as they support operationalized analytics, and enable business value from machine data and other new forms of big data. This TDWI Best Practices report covers how to modernize a DW environment, to keep it competitive and aligned with business goals, in the new age of big data analytics. This report covers: • The many options – both old and new – for modernizing a data warehouse • New technologies, products, and practices to real-world use cases • How to extend the lifespan, range of uses, and value of existing data warehouses
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pentaho, data warehouse, modernization, big data, bug data analytics, best practices, networking, it management, wireless, platforms, data management, business technology
    
Pentaho
Published By: QTS Data Centers     Published Date: Feb 18, 2014
The cloud has been beneficial to many IT departments by enabling quicker responses to the fast-moving market and ever-changing customer needs. So when is your business migrating over?
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qts, the cloud, migrating, integration, technology, it departments, migration, it leaders, it budget, it budget increase, flexibility, scalability, capital expenditures, service provider, cloud provider, big data, analytics, cloud computing, cloud working, application
    
QTS Data Centers
Published 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
Published 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
Published By: Amazon Web Services     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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Amazon Web Services
Published By: Snowflake     Published Date: Jan 25, 2018
"The forces that gave rise to data warehousing in the 1980s are just as important today. However, history reveals the benefits and drawbacks of the traditional data warehouse and how it falls short. This eBook explains how data warehousing has been re-thought and reborn in the cloud for the modern, data-driven organization."
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Snowflake
Published By: Snowflake     Published Date: Jan 25, 2018
If you’re considering your first or next data warehouse, this complimentary eBook explains the cloud data warehouse and how it compares to other data platforms. Download Cloud Data warehouse for Dummies and learn how to get the most out of your data. Highlights include: What a cloud data warehouse is Trends that brought about the adoption of cloud data warehousing How the cloud data warehouse compares to traditional and noSQL offerings How to evaluate different cloud data warehouse solutions Tips for choosing a cloud data warehouse
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Snowflake
Published By: Snowflake     Published Date: Jan 25, 2018
Compared with implementing and managing Hadoop (a traditional on-premises data warehouse) a data warehouse built for the cloud can deliver a multitude of unique benefits. The question is, can enterprises get the processing potential of Hadoop and the best of traditional data warehousing, and still benefit from related emerging technologies? Read this eBook to see how modern cloud data warehousing presents a dramatically simpler but more power approach than both Hadoop and traditional on-premises or “cloud-washed” data warehouse solutions.
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Snowflake
Published By: Infosys     Published Date: Jun 12, 2018
Enterprises often accord the lowest priority for modernizing systems running business-critical applications, for fear of disruption of business as well as the time it would take for the new system to stabilize and come up to speed. A large telecom company had the same fears when they decided to modernize the reporting data warehouse which produced reports critical for making business decisions. See how Infosys helped and the five key takeaways from the project.
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data, fortress, modernize, business, applications, telecom
    
Infosys
Published By: SRC,LLC     Published Date: Jun 01, 2009
To mine raw data and extract crucial insights, business decision‐makers need fast and comprehensive access to all the information stored across their enterprise, regardless of its format or location. Furthermore, that data must be organized, analyzed and visualized in ways that permit easy interpretation of market opportunities growth, shifts and trends and the business‐process changes required to address them. Gaining a true perspective on an organization’s customer base, market area or potential expansion can be a challenging task, because companies use so many relational databases, data warehouse technologies, mapping systems and ad hoc data repositories to gather and house information for a wide variety of specialized purposes.
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src, enterprise, enterprise applications, convergence, compared, counted, combined, reorganized, analyzed, visualized, mapped, database, gis, geographic business intelligence, data independence, etl, csv, delimited text file, mdb (both for microsoft access, esri personal geodatabase
    
SRC,LLC
Published 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, data warehouse, interoperability, analytic environment, data assets, report generation, output options, total cost of ownership, tco, roi, return on investment, olap, enterprise applications
    
SRC,LLC
Published 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, data quality, data warehousing
    
DataFlux
Published By: Oracle Corporation     Published Date: May 11, 2012
This whitepaper provides an overview of Oracle's capabilities for data warehousing and discusses the key features and technologies by which Oracle-based business intelligence and data warehouse systems integrate information and scale to analyze data.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications, data management
    
Oracle Corporation
Published By: Oracle Corporation     Published Date: May 11, 2012
Exadata Hybrid Columnar Compression is an enabling technology for two new Oracle Exadata Storage Server features: Warehouse Compression and Archive Compression. We will discuss each of these features in detail later in this paper, but first let's explore Exadata Hybrid Columnar Compression - the next generation in compression technology.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications, data management
    
Oracle Corporation
Published By: Oracle Corporation     Published Date: May 11, 2012
Exadata is Oracle's fastest growing new product. Much of the growth of Exadata has come at the expense of specialized DW appliance vendors. Take a look at the competitive comparisons these vendors have published and find out why they fall short.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications, data management
    
Oracle Corporation
Published 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, mobile, growth, payback, architecture, demands, enterprise applications, data management, cloud computing, design and facilities
    
Oracle Corporation
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