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data warehouse

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Published By: Oracle     Published Date: Dec 21, 2018
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 Cloudand what it can help organisations achieve. Talking about innovation, security and efficiency, they put the casefor an autonomous future.
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Oracle
Published By: Dell EMC     Published Date: Nov 09, 2015
While the EDW plays an all-important role in the effort to leverage big data to drive business value, it is not without its challenges. In particular, the typical EDW is being pushed to its limits by the volume, velocity and variety of data. Download this whitepaper and see how the Dell™ | Cloudera™ | Syncsort™ Data Warehouse Optimization – ETL Offload Reference Architecture can help.
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Dell EMC
Published By: Collaborative Consulting     Published Date: Dec 23, 2013
There are some surprisingly straightforward reasons behind the glitches, delays, and cost-overruns that can bedevil data warehouse initiatives. ...The first is simply confusing expectations with requirements. But four other troublemakers can also lead to big problems for developers, IT departments, and organizations seeking to maximize the business value of information.
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collaborative consulting, data warehouse, failed projects, business intelligence, business solution, meet expectations, big data, profile importance, cloud vendors, data quality, business goals, complicated architectures, avoid wasted expense, data management, data center
    
Collaborative Consulting
Published By: Collaborative Consulting     Published Date: Jan 15, 2014
When a pharmaceutical company discovered its risks under the new Patient Protection and Affordable Care Act, it turned to Collaborative to comb and consolidate its data. The result: compliance and insight into new business opportunities, too, through a company-wide business data warehouse and enhanced business intelligence.
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collaborative consulting, data, data warehouse, complicane, risk management, business intelligence, consolidation, infrastructure, information management, it management, data integration, collaboration, content integration, data warehousing
    
Collaborative Consulting
Published By: Amazon Web Services     Published Date: Jul 25, 2018
IDC’s research has shown the movement of most IT workloads to the cloud in the coming years. Yet, with all the talk about enterprises moving to the cloud, some of them still wonder if such a move is really cost effective and what business benefits may result. While the answers to such questions vary from workload to workload, one area attracting particular attention is that of the data warehouse. Many enterprises have substantial investments in data warehousing, with an ongoing cost to managing that resource in terms of software licensing, maintenance fees, operational costs, and hardware. Can it make sense to move to a cloud-based alternative? What are the costs and benefits? How soon can such a move pay itself off? Download now to find out more.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Die Recherchen von IDC haben ergeben, dass in den nächsten Jahren die meisten IT-Workloads in die Cloud verschoben werden. Doch neben all den positiven Berichten über Unternehmen, die in die Cloud umziehen, gibt es auch Unternehmen, die sich noch immer fragen, ob ein solcher Wechsel wirklich kosteneffizient ist und welche Vorteile sich aus einem solchen ergeben. Während die Antworten auf solche Fragen von Workload zu Workload variieren, gibt es ein Element, das besondere Aufmerksamkeit auf sich zieht: das Data-Warehouse.
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Amazon Web Services
Published By: TreasureData     Published Date: May 14, 2012
Treasure Data is going to change the way that you think about Big Data and Cloud Data Warehousing. We'd like to get your input on how you see Big Data and Cloud Data Warehousing. Please take our 10 question survey and give us your input.
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treasuredata, data warehousing, cloud, big data, solution, data-driven, tables, queries, analytics, infrastructure, storage, billing, visualization, data management, virtualization, cloud computing, design and facilities
    
TreasureData
Published By: Red Hat     Published Date: Jan 09, 2014
A large enterprise data warehouse company used Red Hat® CloudForms to create a private cloud that includes automated provisioning and self-service for developers and testers. This let them build, test, and release new product versions faster. Find out how in this case study.
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red hat, cloudforms, data, data warehouse, enterprise cloud, cloud management, productivity, private cloud, service delivery, lifecycle management, data warehousing, cloud computing
    
Red Hat
Published By: Juniper Networks     Published Date: Oct 19, 2015
Datacenters are the factories of the Internet age, just like warehouses, assembly lines, and machine shops were for the industrial age. Over the course of the past several years, riding the wave of modernization, datacenters have become the heart and soul of the financial industry, which each year invests over $480 billion in datacenter infrastructure of hardware, software, networks, and security and services.
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juniper, datacenter, threat, ciso, enterprise, data, customer
    
Juniper Networks
Published By: IBM     Published Date: Sep 22, 2011
This white paper highlights the performance and scalability potential of InfoSphere DataStage 8.1 based on a benchmark test simulating a data warehouse scenario. The benchmark is designed to use the profiled situation to provide insight about how InfoSphere DataStage addresses key questions customers frequently ask when designing their information integration architecture.
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ibm, infosphere, data performance and scalability, customers, datastage, application integration, data quality
    
IBM
Published By: Google     Published Date: Oct 26, 2018
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions. This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
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Google
Published By: Google     Published Date: Jan 24, 2019
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions. This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
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Google
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"With the introduction of Oracle Database In-Memory and servers with the SPARC S7 and SPARC M7 processors Oracle delivers an architecture where analytics are run on live operational databases and not on data subsets in data warehouses. Decision-making is much faster and more accurate because the data is not a stale subset. And for those moving enterprise applications to the cloud, Real-time analytics of the SPARC S7 and SPARC M7 processors are available both in a private cloud on SPARC servers or in Oracle’s Public cloud in the SPARC cloud compute service. Moving to the Oracle Public Cloud does not compromise the benefits of SPARC solutions. Some examples of utilizing real time data for business decisions include: analysis of supply chain data for order fulfillment and supply optimization, analysis of customer purchase history for real time recommendations to customers using online purchasing systems, etc. "
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Oracle PaaS/IaaS/Hardware
Published By: Teradata     Published Date: Jan 28, 2015
Althrough Hadoop and related technologies have been with us for several years, most business intelligence (BI) professionals and their business counterparts still harbor a few misconceptions that need to be corrected about Hadoop and related technologies such as MapReduce. This webcast presents the 10 most common myths about Hadoop, then corrects them. The goal is to clarify what Hadoop is and does relative to BI, as well as in which business and technology situations Hadoop-based BI, data warehousing and analytics can be useful.
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teradata, business, intelligence, hadoop, data, integration, analytics, mapreduce, warehouse, best practices, business analytics, business intelligence
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
Our goal is to share best practices so you can understand how designing a data lake strategy can enhance and amplify existing investments and create new forms of business value.
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data lake, data warehouse, enterprise data, migration, enterprise use, data lake strategy, business value, data management, data center
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
This TDWI Checklist Report discusses adjustments to DW architectures that real-world organizations are making today, so that Hadoop can help the DW environment satisfy new business requirements for big data management and big data analytics.
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data, data warehouse, hadoop, hadoop ecosystem, data architectures, data archiving, advanced analytics, data management, data center
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
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teradata, data, big, data, analytics. insights, solutions, business opportunities, challenges, technology, framework, apache, hadoop, architecture, warehouse, optimization, security, scalability, consistency, flexibility, data management
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
Data from the Internet of Things makes an integrated data strategy more vital than ever.
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teradata, internet, things, iot, data, warehouse, analytics, patchwork, value, development, integration, supply chain, inventory, sales, market demand, channel partners, deployment, hadoop, aster, discovery
    
Teradata
Published By: RedPoint Global     Published Date: May 11, 2017
While they’re intensifying, business-data challenges aren’t new. Companies have tried several strategies in their attempt to harness the power of data in ways that are feasible and effective. The best data analyses and game-changing insights will never happen without the right data in the right place at the right time. That’s why data preparation is a non-negotiable must for any successful customer-engagement initiative. The fact is, you can’t simply load data from multiple sources and expect it to make sense. This white paper examines the shortcomings of traditional approaches such as data warehouses/data lakes and explores the power of connected data.
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customer engagement, marketing data, marketing data analytics, customer data platform
    
RedPoint Global
Published By: Veritas & PureStorage     Published Date: Jun 23, 2017
As flash costs continue to drop and new, flash-driven designs help to magnify the compelling economic advantages AFAs offer relative to HDD-based designs, mainstream adoption of AFAs —first for primary storage workloads and then ultimately for secondary storage workloads — will accelerate. Well-designed AFAs that still leverage legacy interfaces like SAS will be able to meet many performance requirements over the next year or two. Those IT organisations that aim to best position themselves to handle future growth will want to look at next-generation AFA offerings, as the future is no longer flash-optimised architectures (implying that HDD design tenets had to be optimised around) — it is flash-driven architectures.
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cloud data, online marketing, customer acquisition, mobile marketing, social marketing, data warehouse, data storage, data collection
    
Veritas & PureStorage
Published By: Attunity     Published Date: Jan 14, 2019
This whitepaper explores how to automate your data lake pipeline to address common challenges including how to prevent data lakes from devolving into useless data swamps and how to deliver analytics-ready data via automation. Read Increase Data Lake ROI with Streaming Data Pipelines to learn about: • Common data lake origins and challenges including integrating diverse data from multiple data source platforms, including lakes on premises and in the cloud. • Delivering real-time integration, with change data capture (CDC) technology that integrates live transactions with the data lake. • Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assemble a historical data store. • Leveraging a scalable and autonomous streaming data pipeline to deliver analytics-ready data sets for better business insights. Read this Attunity whitepaper now to get ahead on your data lake strategy in 2019.
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data lake, data pipeline, change data capture, data swamp, hybrid data integration, data ingestion, streaming data, real-time data, big data, hadoop, agile analytics, cloud data lake, cloud data warehouse, data lake ingestion, data ingestion
    
Attunity
Published By: Attunity     Published Date: Feb 12, 2019
Read this checklist report, with results based on the Eckerson Group’s survey and the Business Application Research Center (BARC), on how companies using the cloud for data warehousing and BI has increased by nearly 50%. BI teams must address multiple issues including data delivery, security, portability and more before moving to the cloud for its infinite scalability and elasticity. Read this report to understand all 7 seven considerations – what, how and why they impact the decision to move to the cloud.
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cloud, business intelligence, analytics, cloud data, data lake, data warehouse automation tools, dwa, data warehouse, security and compliance, data movement, hybrid cloud, hybrid cloud environment, cross-platform automation, portability
    
Attunity
Published By: Attunity     Published Date: Feb 12, 2019
How can enterprises overcome the issues that come with traditional data warehousing? Despite the business value that data warehouses can deliver, too often they fall short of expectations. They take too long to deliver, cost too much to build and maintain, and cannot keep pace with changing business requirements. If this all rings a bell, check out Attunity’s knowledge brief on data warehouse automation with Attunity Compose. The solution automates the design, build, and deployment of data warehouses, data marts and data hubs, enabling more agile and responsive operation. The automation reduces time-consuming manual coding, and error-prone repetitive tasks. Read the knowledge brief to learn more about your options.
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dwa, data warehouse automation, etl development, extract transform load tools, etl tools, data warehouse, data marts, data hubs data warehouse lifecycle, data integration, change management, data migration, consolidating data, cloud data warehousing, data warehouse design, attunity compose
    
Attunity
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
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