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warehousing

Results 51 - 75 of 162Sort Results By: Published Date | Title | Company Name
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: 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.
Tags : 
oracle, data, analytical data, data marts, industry-specific data warehouse, financial services
    
Oracle
Published By: Staples     Published Date: Jan 29, 2018
As a Staples Business Advantage® customer, exclusive content is just one of the perks you can access any time. In this white paper, industrial shipping expert Dave Fromm looks at how the industry has transformed over the last 20 years and how to save money, warehousing space and more. Get your copy today.
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industrial shipping, staples business advantage, warehousing
    
Staples
Published By: SAP Inc.     Published Date: Jun 16, 2009
This paper reviews the current status of MDM, and offers suggestions for planning, building and deploying an MDM environment.
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sap, master data management, mdm, business intelligence, data warehousing, sor, system of record, enterprise mdm, mds, mms
    
SAP Inc.
Published By: AWS     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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AWS
Published By: Motorola Solutions     Published Date: Apr 26, 2019
When it comes to enhancing worker safety, boosting productivity and streamlining operations, communication matters. Organizations across various industry segments — trucking, rail, airlines and airports, and warehousing/distribution — are juggling a mix of communication devices and hindered by gaps in coverage, poor battery life and fragile equipment that cannot withstand harsh environmental conditions.
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Motorola Solutions
Published By: Motorola Solutions     Published Date: Apr 26, 2019
ffective communications are the foundation for any good team, and the transportation and logistics (T&L) sector is no exception. Charged with managing the warehousing, inventory, and movement of freight across the supply chain — both through internal and external distribution networks — T&L professionals rely on high levels of team collaboration to get the job done right. By helping companies leverage the knowledge, talents, and insights of their people, effective team communications ensures that customers get their deliveries when, how, and where they want them. Meeting those expectations in today’s fast-paced, demanding distribution environment requires reliable, clear voice and data logistics communications that start at the warehouse and end at the point of delivery. In this white paper, we explore the key challenges that T&L companies are facing in today’s business environment and hear how instant push-to-talk and advanced video surveillance can help them develop streamlined suppl
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Motorola Solutions
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. 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. Amazon 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. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set: • Enterprise-class relational database query and management system • Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools • Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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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: InfoSys     Published Date: Apr 08, 2013
Big Data: Too Much information, Too Little illumination; As enterprises go about their Big Data adoption journey, there are many pressing questions at hand. What are the Big Data capabilities they desire?
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big data, business intelligence, analytics, data warehousing, infosys
    
InfoSys
Published By: AWS     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. 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 q
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AWS
Published By: Zebra Technologies     Published Date: May 02, 2018
The exponential growth of omnichannel shopping and ever-burgeoning demand for faster merchandise deliveries is redefining the supply chain’s distribution of consumer products goods. A seminal shift in how shoppers increasingly buy via multiple touch points — online from desktop computers, mobile devices and in-store — has created the need for the “smarter” warehouse to serve today’s connected consumer. As retailers look to merge their brick-and-mortar and online operations to cut costs and boost efficiency, warehouse management systems must keep pace. For this report, Zebra Technologies analyzed the state of the warehousing marketplace among firms in the US and Canada with at least $15 million in annual revenues. The online survey asked IT and operations personnel in the manufacturing, retail, transportation and wholesale market segments to share their insights and business plans over the next five years, in light of a rapidly changing industry.
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warehouse, merchandise, delivery, demand, devices, computers
    
Zebra Technologies
Published By: MemSQL     Published Date: Nov 15, 2017
Data Warehousing in the Age of Artificial Intelligence This Ebook will guide you through building and deploying scalable, production-ready artificial-intelligence applications. Inside, you will find several artificial intelligence use cases, code samples to help you get started, and an outline of high throughput data processing architectures necessary for developing AI applications.
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data warehousing, artificial intelligence, data piplelines
    
MemSQL
Published By: Snowflake     Published Date: Jan 25, 2018
"The forces that gave rise to data warehousing in the 1980s are just as important today. However, history reveals the benefits and drawbacks of the traditional data warehouse and how it falls short. This eBook explains how data warehousing has been re-thought and reborn in the cloud for the modern, data-driven organization."
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Snowflake
Published By: Snowflake     Published Date: Jan 25, 2018
If you’re considering your first or next data warehouse, this complimentary eBook explains the cloud data warehouse and how it compares to other data platforms. Download Cloud Data warehouse for Dummies and learn how to get the most out of your data. Highlights include: What a cloud data warehouse is Trends that brought about the adoption of cloud data warehousing How the cloud data warehouse compares to traditional and noSQL offerings How to evaluate different cloud data warehouse solutions Tips for choosing a cloud data warehouse
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Snowflake
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: Tidal Software     Published Date: Sep 03, 2008
Businesses can gain greater value from their BI investment by improving the way in which data flows to the BI system are managed. Many problems result when the ETL process is handled by a patchwork of scripting, custom coding, and various built-in schedulers that are part of existing ETL solutions, because these systems do not provide end-to-end execution, monitoring, and control of the ETL process.
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business intelligence, data warehousing, data management, data integration, etl, extract–transform-load, bi operations, bi management, informatica, cognos, ibm datastage, sas, business objects, crystal enterprise, tidal, tidal software
    
Tidal Software
Published By: Oracle Corporation     Published Date: May 11, 2012
Download this white paper to learn about the market-leading eCommerce solution and how improved performance can result in increased conversion rates and higher revenue.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications
    
Oracle Corporation
Published By: Oracle Corporation     Published Date: May 11, 2012
By using the Oracle Big Data Appliance with Oracle Exadata, enterprises can acquire, organize and analyze all their data to make the most informed decisions. Read this whitepaper to learn about all of the key benefits of Oracle Big Data Appliance.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications
    
Oracle Corporation
Published By: Oracle Corporation     Published Date: May 11, 2012
This whitepaper provides an overview of Oracle's capabilities for data warehousing and discusses the key features and technologies by which Oracle-based business intelligence and data warehouse systems integrate information and scale to analyze data.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications
    
Oracle Corporation
Published By: Oracle Corporation     Published Date: May 11, 2012
Exadata Hybrid Columnar Compression is an enabling technology for two new Oracle Exadata Storage Server features: Warehouse Compression and Archive Compression. We will discuss each of these features in detail later in this paper, but first let's explore Exadata Hybrid Columnar Compression - the next generation in compression technology.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications
    
Oracle Corporation
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