data warehouse

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Published By: Teradata     Published Date: May 02, 2017
Read this article to discover the 4 things no data warehouse should be without.
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cloud data, cloud security, cloud management, storage resource, computing resources, data warehousing, data storage, cloud efficiency
    
Teradata
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: Teradata     Published Date: Jan 16, 2015
This Neil Raden paper describes the current need for data warehousing, why SAP® BW is an incomplete choice and how Teradata Analytics for SAP® Solutions is a superior option. Download now!
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teradata, sap solutions, data warehouse, extracted data, data management
    
Teradata
Published By: IBM     Published Date: Jul 05, 2016
In an environment where data is the most critical natural resource, speed-of-thought insights from information and analytics are a critical competitive imperative.
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ibm, data warehouse, big data, analytics, data warehouse, business intelligence, knowledge management, data management, data center, data science, data storage, data visualization
    
IBM
Published By: Teradata     Published Date: May 02, 2017
A Great Use of the Cloud: Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs. This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.
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data projects, data volume, business data, cloud security, data storage, data management, cloud privacy, encryption, security integration
    
Teradata
Published By: IBM     Published Date: Mar 29, 2017
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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cloud, analytics, data, organization, ibm
    
IBM
Published By: IBM     Published Date: May 30, 2008
WinterCorp analyzes IBM's DB2 Warehouse and how it addresses twin challenges facing enterprises today: improving the value derived from the torrents of information processed every day, while lowering costs at the same time. Discover why WinterCorp believes the advances in data clustering strategies and intelligent software compression algorithms in IBM's Data Warehouse improves performance of business intelligence queries by radically reducing the I/O's needed to resolve them.
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data warehousing, data management, database management, database administration, dba, business intelligence, ibm, leveraging information, li campaign, ibm li
    
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
    
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, data center
    
IBM
Published 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
    
SAS
Published By: Network Automation     Published Date: Dec 08, 2008
Often, the insurance underwriters stay late that last day of the month to enter new policies. This means that IT staff have a very small window to execute the applications critical to successful and error-free closing of the accounting books. IT staff had to run and baby sit the applications – one application requiring manual operation took over three hours to complete, another that uploads premium and claim information to the data warehouse took up to six hours.
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network automation, application execution, process automation, networking, it management, data management
    
Network Automation
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: 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 center design and management
    
IBM
Published By: NEC     Published Date: Aug 26, 2014
In addition to high reliability and availability, enterprise mission critical applications, data centers operating 24x7, and data analysis platforms all demand powerful data processing capabilities and stability. The NEC PCIe SSD Appliance for Microsoft® SQL Server® is a best-practice reference architecture for such demanding workloads. It comprises an Express 5800 Scalable Enterprise Server Series with Intel® Xeon® processor E7 v2 family CPUs, high-performance HGST FlashMAX II PCIe server-mounted flash storage, and Microsoft® SQL Server® 2014. When compared with the previous reference architecture based on a server with the Intel® Xeon® processor E7 family CPUs, benchmark testing demonstrated a performance improvement of up to 173% in logical scan rate in a data warehouse environment. The testing also demonstrated consistently fast and stable performance in online transaction processing (OLTP) that could potentially be encountered.
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sql, datacenter, servers, virtualization, customer value, analytics, application owners, system integrators, big data, reliability, enterprise, availability, serviceability, processor, enterprise applications, storage
    
NEC
Published By: Oracle Corporation     Published Date: Mar 03, 2011
This white paper discusses how by designing these three corner stones correctly, you can seamlessly scale out your EDW without having to constantly tune or tweak the system.
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datawarehousing, system management, oracle
    
Oracle Corporation
Published By: Oracle Corporation     Published Date: May 11, 2012
By using the Oracle Exadata Database Machine as your data warehouse platform you have a balanced, high performance hardware configuration. This paper focuses on the other two corner stones, data modeling and data loading.
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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: Attivio     Published Date: Aug 20, 2010
With the explosion of unstructured content, the data warehouse is under siege. In this paper, Dr. Barry Devlin discusses data and content as two ends of a continuum, and explores the depth of integration required for meaningful business value.
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attivio, data warehouse, unified information, data, content, unstructured content, integration, clob, blob
    
Attivio
Published By: Vertica     Published Date: Aug 15, 2010
If you are responsible for BI (Business Intelligence) in your organization, there are three questions you should ask yourself: - Are there applications in my organization for combining operational processes with analytical insight that we can't deploy because of performance and capacity constraints with our existing BI environment?
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business intelligence, vertica, aggregated data, olap, rolap, sql, query, data warehouse, oltp
    
Vertica
Published By: IBM     Published Date: Jul 24, 2008
Tune into this TDWI Radio News interview, with Eric Kavanagh, to hear Karen Parrish, vice president of business intelligence solutions for IBM, as she explains Big Blue's take on the evolving industry of data warehouse appliances.
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IBM
Published By: SAP     Published Date: May 18, 2014
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
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sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
    
SAP
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: Pentaho     Published Date: Jan 16, 2015
If you’re considering a big data project, this whitepaper provides an overview of current common use cases for big data, from entry-level to more complex. You’ll get an in-depth look at some of the most common, including data warehouse optimization, streamlined data refinery, monetizing your data, and getting a 360 degree view of your customer. For each, you’ll discover why companies are investing in them, what the projects look like, and key project considerations, including tools and platforms.
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big data, nosql, hadoop, data integration, data delivery, data management, data center
    
Pentaho
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.
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