warehouse

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Published By: IBM     Published Date: Nov 18, 2013
Speed, simplicity and affordability: 3 capabilities businesses need from their warehousing environment. IBM DB2 with BLU Acceleration gives organizations a complete, multipurpose environment to rapidly distill insight from their data, make timely decisions and capitalize on opportunities.
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ibm, ibm db2, blu acceleration, speed, acceleration, warehouse, warehousing environment, multipurpose environment, data, data management, data warehouse, warehouse performance, productivity, data center
    
IBM
Published By: IBM     Published Date: Jan 09, 2014
According to Dr. Barry Devlin of 9sight Consulting, the truth behind all the talk about big data and the possibilities it can offer is not hard to see, provided that organizations are willing to return to the principles of good data management processes.
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ibm, big data, 9sight consulting, data, it management, maximize business, deployment, business opportunities, big data usage, data warehouse, data center, business analytics, big data offerings, core business data, analytic data, puredata system, data virtualization, data integration, data types, data quality
    
IBM
Published 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, data generation, data management, storage, acceleration, business intelligence, data warehouse, analytical applications, data mining, data warehousing
    
IBM
Published By: IBM     Published Date: Apr 16, 2014
Speed, simplicity and affordability: 3 capabilities businesses need from their warehousing environment. IBM DB2 with BLU Acceleration gives organizations a complete, multipurpose environment to rapidly distill insight from their data, make timely decisions and capitalize on opportunities
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ibm, data warehouse, warehouse environment, speed, simplicity, affordibility, db2 with blu, acceleration, data warehousing, data system, data storage, infrastructure
    
IBM
Published By: IBM     Published Date: May 02, 2014
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.
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ibm, big data platform, architecting big data, analytics, intelligent business strategies, data complexity, data types, workload growth, workload complexity, big data analytic applications, operational decisions, multi-structured data, querying data, scalable data management, analytical ecosystem, hadoop solutions, it management, data management, best practices, business intelligence
    
IBM
Published 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
Published 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, business intelligence
    
IBM
Published By: IBM     Published Date: Jan 26, 2015
IBM Bluemix, a robust platform as a service (PaaS) to host and deploy your app, also provides a wide range of enterprise grade tools that can be used in your applications to run your business needs. The Analytics Warehouse Service available in IBM Bluemix provides a powerful, easy-to-use, and agile platform for business intelligence (BI) and analytics tasks. Check out this upcoming webcast to learn how you can create a ready-to-use BI and analytics service on Bluemix, in just a few clicks, and even access those results on an Android app.
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analytics service, open cloud platform, ibm, web developers, mobile developers, business integration, security, it management, enterprise applications, data management, data center
    
IBM
Published By: IBM     Published Date: Apr 29, 2015
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative.
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data warehouse, big data, unstructured data, implementations, business intelligence, database development, quality assurance, productivity, data warehousing
    
IBM
Published By: IBM     Published Date: Apr 29, 2015
First generation warehouses were not designed to manage data at today's volume or variety. Coercing older technologies to satisfy new demands can be inefficient, burdensome and costly. Read how IBM PureData System for Analytics is built for simplicity and speed.
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big data, data management, hardware, business intelligence, analytical applications, database development, productivity
    
IBM
Published By: IBM     Published Date: Jul 07, 2015
Learn about information integration and governance for data warehousing and big data and analytics.
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data warehouse, bad data, big data, mobility, compute-intensive apps, virtualization, cloud computing, scalable infrastructure, reliability, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 14, 2015
This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, data warehouse, data center, information governance, analytics, big data analytics, business management, data management
    
IBM
Published By: IBM     Published Date: Jun 19, 2014
Download this ebook to learn the requirements for delivering trusted information to a modern data warehouse.
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ibm, big data, data warehousing, data, best practices, application integration, business metrics, business intelligence
    
IBM
Published By: IBM     Published Date: Sep 15, 2014
Download this ebook to learn the requirements for delivering trusted information to a modern data warehouse and the guiding principles for trusted information in next generation data warehouse environments.
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ibm, data warehouse, data warehousing, hadoop, trusted data, data, data deduplication, data center design and management
    
IBM
Published By: IBM     Published Date: Jun 10, 2014
Download this short paper for an overview of the benchmark, the result and understand the importance to businesses deploying Hadoop into their data warehouse environments.
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ibm, big sql, hadoop, sql, application integration, database development, data integration, data warehousing
    
IBM
Published By: IBM     Published Date: Nov 13, 2014
This paper focuses on the benefits of a queryable data store and big data technologies available to support data warehouse modernization. Read the paper to understand how data can be stored and optimized with Hadoop.
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ibm, ibm infosphere, big data, hadoop, big sql, data warehousing, storage management, analytical applications, data deduplication
    
IBM
Published By: IBM     Published Date: Nov 09, 2015
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative.
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ibm, data warehouse, big data, data management, data center
    
IBM
Published By: IBM     Published Date: Nov 09, 2015
IBM believes the Data Warehouse market continues to expand and adapt to address new requirements for user self-service, increased agility, requirements for new data types, lower cost solutions, adoption of open source, driving better business insight, and faster time to value.
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ibm, data, magic quadrant, data management, analytics
    
IBM
Published By: IBM     Published Date: Nov 16, 2015
As vendors continue to evolve their solutions to fit these changing requirements, IBM remains a leader in this Gartner Magic Quadrant.
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ibm, data warehouse, data management, analytics, gartner, data center
    
IBM
Published 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
Published By: IBM     Published Date: Apr 19, 2016
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse. In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
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ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, data management
    
IBM
Published By: IBM     Published Date: May 17, 2016
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
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ibm, big data, inline analytics, business analytics, roi, business intelligence
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
Cloud-based data warehousing as-a-service, built for analytics
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ibm, dashdb, data, analytics, data warehouse, cloud, analytics, business insights, knowledge management, enterprise applications, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse. In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
Tags : 
ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, knowledge management, data management, data center
    
IBM
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