data warehouse

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Published By: StreamSets     Published Date: Sep 24, 2018
Treat data movement as a continuous, ever-changing operation and actively manage its performance. Before big data and fast data, the challenge of data movement was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line.
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practices, modern, data, performance
    
StreamSets
Published By: NAVEX Global     Published Date: Mar 21, 2018
Good analysis and benchmarking of hotline data helps organisations answer crucial questions about their ethics and compliance programme. Comparing internal data year over year to help answer these questions is important. But getting a broader perspective on how your performance matches up to industry norms is critical. To help, each year NAVEX Global takes anonymised data collected through our hotline and incident management systems to create these reports. This particular report is the second NAVEX Global benchmark report we have published that focuses specifically on the status of ethics and compliance hotline services in the EMEA and APAC regions. This benchmark only takes reporting data from organisations that has its data warehoused in Europe—a subset of the data used in our global hotline report.
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NAVEX Global
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: StreamSets     Published Date: Sep 24, 2018
The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.
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replatforming, age, data, lake, apache, hadoop
    
StreamSets
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
    
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: 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
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: 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, data integration, information management
    
IBM
Published By: Cambridge Semantics     Published Date: Aug 17, 2015
As the quantity and diversity of relevant data grows within and outside of the enterprise, business users and IT are struggling to extract maximum value from this data. Current approaches, including the rigid relational data warehouse and the unwieldy Hadoop-only Data Lake, are limited in their ability to provide users and IT with the answers they need with the proper governance and security required. Read this whitepaper to learn how The Anzo Smart Data Lake from Cambridge Semantics solves these problems by disrupting the way IT and business alike manage and analyze data at enterprise scale with unprecedented flexibility, insight and speed.
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Cambridge Semantics
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: 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, service oriented architecture, data warehousing
    
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
    
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, business intelligence
    
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, design and facilities
    
Oracle Corporation
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: 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, business intelligence, database development, data quality, data warehousing
    
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, analytical applications, database development, data integration, data mining, data quality, service oriented architecture, information management, data warehousing
    
Vertica
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