data warehouse

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Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18
Published By: Oracle     Published Date: Sep 21, 2018
Agility and speed are required in the cloud economy. Modernize data warehouses with built-in adaptive machine learning to eliminate manual labor for administrative tasks. With Oracle, businesses can now build data warehouses or data marts in minutes.
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Oracle
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, 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, 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, business technology
    
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, business technology
    
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: 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: Amazon Web Services     Published Date: Aug 20, 2018
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 querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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Amazon Web Services
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: Amazon Web Services     Published Date: Sep 05, 2018
AbeBooks, with Amazon Redshift, has been able to upgrade to a comprehensive data warehouse with the enlistment of Matillion ETL for Amazon Redshift. In this case study, we share AbeBooks’ data warehouse success story.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technolog
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Amazon Web Services
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
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