warehouse

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Published By: TopQuadrant     Published Date: Jun 01, 2017
This paper presents a practitioner informed roadmap intended to assist enterprises in maturing their Enterprise Information Management (EIM) practices, with a specific focus on improving Reference Data Management (RDM). Reference data is found in every application used by an enterprise including back-end systems, front-end commerce applications, data exchange formats, and in outsourced, hosted systems, big data platforms, and data warehouses. It can easily be 20–50% of the tables in a data store. And the values are used throughout the transactional and mastered data sets to make the system internally consistent.
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TopQuadrant
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: Cloudant - an IBM Company     Published Date: Jun 01, 2015
Whether you're a DBA, data scientist or developer, you're probably considering how the cloud can help modernize your information management and analytics strategy. Cloud data warehousing can help you get more value from your data by combining the benefits of the cloud - speed, scale, and agility - with the simplicity and performance of traditional on-premises appliances. This white paper explores how a cloud data warehouse like IBM dashDB can reduce costs and deliver new business insights. Readers will learn about: - How data warehousing-as-a-service helps you scale without incurring extra costs - The benefits of in-database analytics in a cloud data warehouse - How a cloud data warehouse can integrate with the larger ecosystem of business intelligence tools, both on prem and off prem
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nosql, ibm, dashdb, database, cloud
    
Cloudant - an IBM Company
Published By: iCEDQ     Published Date: Feb 05, 2015
The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.
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data, data management, data warehousing, data quality, etl testing, malcolm chisholm
    
iCEDQ
Published By: WhereScape     Published Date: Mar 16, 2016
Industry expert Wayne Eckerson provides an overview of the emerging data warehouse automation market and outlines the value of using automation tools for developing data warehouses, data marts, analytical environments and big data platforms. Eckerson details WhereScape’s architecture—which enables a data-driven approach to automation. Eckerson also discusses how agility and automation together encourage iterative development and closer collaboration between business and IT.
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WhereScape
Published By: WhereScape     Published Date: Aug 18, 2016
Data Vault 2.0 leverages parallel database processing for large data sets and provides an extensible approach to design that enables agile development. WhereScape provides data warehouse automation software solutions that enable Data Vault agile project delivery through accelerated development, documentation and deployment without sacrificing quality or flexibility.
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WhereScape
Published By: Snowflake Computing     Published Date: Apr 19, 2016
Data warehouse as a service brings scalability and flexibility to organizations seeking to deliver data to all users and systems that need to analyze it. The ability to access and analyze data is the critical foundational element for competing in new and old industries alike. Yet, a recent survey of IT executives finds that most are still struggling— and frustrated — with widely used data analytics tools. Find out what your peers are saying, and see how your data analytics environment compares.
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Snowflake Computing
Published By: Snowflake Computing     Published Date: Feb 27, 2017
Snowflake’s cloud-built data warehouse delivers the performance, concurrency, simplicity and affordability needed to store and analyze all of an organization’s data in one location. Snowflake combines the power of data warehousing, the flexibility of big data platforms and the elasticity of the cloud. Find out more at snowflake.net.
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Snowflake Computing
Published By: TopQuadrant     Published Date: Apr 16, 2013
TopBraid Insight provides an ‘out-of-the-box’ Logical Data Warehouse that allows an agile, extensible approach to querying data from diverse data sources. This white paper describes the approach as applied to the Life Sciences industry.
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TopQuadrant
Published By: Oracle     Published Date: Jan 03, 2019
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloud and what it can help organisations achieve. Talking about innovation,security and efficiency, they put the case for an autonomous future. Watch the webinar.
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Oracle
Published By: BMC ASEAN     Published Date: Dec 18, 2018
Big data projects often entail moving data between multiple cloud and legacy on-premise environments. A typical scenario involves moving data from a cloud-based source to a cloud-based normalization application, to an on-premise system for consolidation with other data, and then through various cloud and on-premise applications that analyze the data. Processing and analysis turn the disparate data into business insights delivered though dashboards, reports, and data warehouses - often using cloud-based apps. The workflows that take data from ingestion to delivery are highly complex and have numerous dependencies along the way. Speed, reliability, and scalability are crucial. So, although data scientists and engineers may do things manually during proof of concept, manual processes don't scale.
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BMC ASEAN
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: Attivio     Published Date: Aug 20, 2010
Current methods for accessing complex, distributed information delay decisions and, even worse, provide incomplete insight. This paper details the impact of Unified Information Access (UIA) in improving the agility of information-driven business processes by bridging information silos to unite content and data in one index to power solutions and applications that offer more complete insight.
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attivio, data warehouse, unified information, data, content, unstructured content, integration, clob, blob, database development, data warehousing
    
Attivio
Published By: Information Builders     Published Date: Sep 24, 2008
Business Intelligence helps retailers, warehouse staff, customer services agents, and your value chain realize new innovations, improve margins, and propel profits to new heights. Learn how Ace Hardware, Food Lion, and others leverage our software.
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information builders, infobuilders, business intelligence, soa, roi, crm, marketing value
    
Information Builders
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, analytical applications
    
SAP
Published By: Oracle     Published Date: Nov 28, 2017
Today’s leading-edge organizations differentiate themselves through analytics to further their competitive advantage by extracting value from all their data sources. Other companies are looking to become data-driven through the modernization of their data management deployments. These strategies do include challenges, such as the management of large growing volumes of data. Today’s digital world is already creating data at an explosive rate, and the next wave is on the horizon, driven by the emergence of IoT data sources. The physical data warehouses of the past were great for collecting data from across the enterprise for analysis, but the storage and compute resources needed to support them are not able to keep pace with the explosive growth. In addition, the manual cumbersome task of patch, update, upgrade poses risks to data due to human errors. To reduce risks, costs, complexity, and time to value, many organizations are taking their data warehouses to the cloud. Whether hosted lo
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Oracle
Published By: Cisco     Published Date: Jun 21, 2016
Imagine you manage a warehouse for a small shipping firm. You have a simple job: route packages. But your employer expands to 25 western cities and 50 locations. After further expansion you now need to track packages coming by truck, rail, and plane and route them based on contents and weight to 10,000 different locations worldwide. While keeping traffic in and out tightly secured, making sure each package reaches its destination on time, and without going over budget.
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Cisco
Published By: Epicor     Published Date: Aug 28, 2018
By now, mobile technology has become an essential part of people’s lives. As both consumers and staff trend more toward a younger, digitally savvy demographic, lumber and building materials (LBM) businesses need to take advantage of mobile tools or risk losing to the competition. Mobile technologies can bring incredible benefits to LBM enterprises for delivery and dispatch, field sales, the selling floor, and the warehouse. To better help you seize the mobile advantage, Epicor has identified eight ways your LBM business can leverage mobile technologies to foster growth, including: • Serving your customers with the latest information • Serving your customers with timely, accurate deliveries • Driving revenue with increased efficiency • Streamlining operations Check out this Epicor tipsheet and discover how else your LBM business can benefit from mobile technologies.
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lumber, building materials, lbm, erp, bistrack, lumber distributors
    
Epicor
Published By: AWS     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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AWS
Published By: AWS     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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AWS
Published By: Oracle CX     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 CX
Published By: Oracle CX     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 CX
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