warehouses

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Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache
    
Altiscale
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: 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: AWS - ROI DNA     Published Date: Jun 12, 2018
Traditional databases and data warehouses are evolving to capture new data types and spread their capabilities in a hybrid cloud architecture, allowing business users to get the same results regardless of where the data resides. The details of the underlying infrastructure become invisible. Self-managing data lakes automate the provisioning, reliability, performance and cost, enabling data access and experimentation.
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AWS - ROI DNA
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: Borer Data Systems Ltd.     Published Date: Nov 07, 2007
Clear Image was awarded a contract to supply and fit CCTV and Access control to NISA, one of the largest picking warehouses in Europe. The company runs 3 shifts per day and wanted to allocate lockers to employees. The simple solution would have been to give each employee a locker, but between Borer and Clear Image, a better solution was devised. Thanks to our technology, we can create one to many relationships between our devices. 
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security, security management, access control, identity management, iam, authentication, nisa, physical security, tracking, workforce, workforce management, employee management, borer, nisa, clear image, borer, borer data systems, access control, muster, roll call
    
Borer Data Systems Ltd.
Published By: Business Software     Published Date: Apr 04, 2013
Today, there is unprecedented pressure on companies in all industry sectors to keep their supply chains running smoothly. This often means that these businesses with their ever-changing distribution channels and production centers feeding those supply chains, need to efficiently manage their warehouses. In this whitepaper, Business-software.com profiles the leading warehouse management software vendors.
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top 10 warehouse management software, vendors, revealed, unprecedented pressure, companies, industry, sectors, supply chains, business-software.com
    
Business Software
Published By: CA Technologies EMEA     Published Date: Apr 10, 2018
Effective Competition Depends on Continuous Delivery of Quality Software In today’s application economy every company is a software company, no matter what industry it is in: • Shipping companies depend on logistics software to efficiently route packages, arrange drivers and automate warehouses. • Retail companies rely on software to manage inventory, engage with customers online and to give in-store associates the tools they need to answer customer questions on the spot. • Marketing firms lean on applications to gather consumer data and parse it, automate communication with prospects and effectively manage advertising campaigns. The examples are endless. The point is that in order to compete today, every business must be able to quickly build and tweak software to adjust to always evolving market demands. Ultimately, business success depends on faster development iterations while still maintaining the high quality of service expected by customers, stakeholders and end users.
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CA Technologies EMEA
Published By: DataFlux     Published Date: Jan 07, 2011
This white paper introduces and examines a breakthrough platform solution designed to drive parallel-process data integration - without intensive pre-configuration - and support full-lifecycle data management from discovery to retirement.
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dataflux, enterprise data, data integration, configuration, lifecycle data management, data warehouses
    
DataFlux
Published By: Epson     Published Date: Nov 20, 2017
A customer may store heavy file boxes in one of its warehouses, but Fireproof Records Center spends a lot of time strategizing about the paperless office. Based in Grove City, Ohio, the company helps businesses in central Ohio manage information more efficiently, offering a suite of cloud document management and scanning tools, including the easy-to-use Epson WorkForce® color document scanner.
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Epson
Published By: Epson     Published Date: Nov 21, 2017
A customer may store heavy file boxes in one of its warehouses, but Fireproof Records Center spends a lot of time strategizing about the paperless office. Based in Grove City, Ohio, the company helps businesses in central Ohio manage information more efficiently, offering a suite of cloud document management and scanning tools, including the easy-to-use Epson WorkForce® color document scanner.
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Epson
Published By: Gigaom     Published Date: Sep 17, 2019
Today’s data volumes, combined with the credo of data-driven everything, make for an analytics landscape radically different from that of years past. Delays involved in moving and shaping operational data into separate data warehouses, data lakes and standalone BI platforms can thwart effective operational analytics. In fact, for some organizations, such segregation of infrastructure and process may not work at all. Just because OLTP (Online Transactional Processing) and OLAP (Online Analytical Processing) are distinct workloads doesn’t mean they should take place on separate platforms. Sure, dedicated server nodes may make sense to optimize the performance of operational and analytical tasks, but they need to operate on the same data, in a coordinated fashion. Modern databases – serving the needs of both business analysts and application developers – are great platforms for implementing such business-forward architectures. To learn more, join us for this free 1-hour webinar from Gig
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Gigaom
Published By: Gigaom     Published Date: Sep 16, 2019
We’ve heard it before. A data warehouse is a place for formally-structured, highly-curated data, accommodating recurring business analyses, whereas data lakes are places for “raw” data, serving analytic workloads, experimental in nature. Since both conventional and experimental analysis is important in this data-driven era, we’re left with separate repositories, siloed data, and bifurcated skill sets. Or are we? In fact, less structured data can go into your warehouse, and since today’s data warehouses can leverage the same distributed file systems and cloud storage layers that host data lakes, the warehouse/lake distinction’s very premise is rapidly diminishing. In reality, business drivers and business outcomes demand that we abandon the false dichotomy and unify our data, our governance, our analysis, and our technology teams. Want to get this right? Then join us for a free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest, Dav
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Gigaom
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: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18
Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
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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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
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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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
One of the biggest changes faces organizations making purchasing and deployment decisions about analytic databases -- including relational data warehouses -- is whether to opt for a cloud solution.
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Group M_IBM Q2'19
Published By: IBM     Published Date: Dec 30, 2008
Most long-standing data warehouses are designed to support a relatively small number of users who access information to support strategic decisions, financial planning and the production of standard reports that track performance. Today, many more users need to access information in context and on demand so that critical functions are optimized to run efficiently. Learn how to create a roadmap for a truly dynamic warehousing infrastructure, and move ahead of your competition with your business intelligence system
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warehousing infrastructure, ibm, business intelligence, data warehouse, dynamic warehousing, data warehouse model, master data
    
IBM
Published By: IBM     Published Date: Feb 02, 2009
A comprehensive solution for leveraging data in today's retail environment. From customer data to product placement statistics, retail organizations are constantly juggling information. As the sheer amount of data continues to grow, it becomes increasingly difficult to manage. Not only does data come in many different forms—such as reports, memos and e-mails—but often it’s scattered across multiple repositories.
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ibm, ibm balanced warehouses, ibm master data management server, ibm omnifind, ibm industry data models, dynamic warehousing, retail buyer’s guide, leveraging data, customer data, product placement statistics, data management, scalable system, customer relationships, cross-sell, up-sell opportunities, db2 warehouse, oltp-based transactional data server, information management software
    
IBM
Published By: IBM     Published Date: Feb 02, 2009
A comprehensive solution for leveraging data in today's financial industry. Most organizations realize that the key to success lies in how well they manage data—and the banking industry is no exception. From customer statistics to strategic plans to employee communications, financial institutions are constantly juggling endless types of information.
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ibm, information management software, leveraging data, dynamic warehousing, data management, improve customer service, real-time risk analysis, analytics capabilities, information on demand framework, ibm db2 warehouse, ibm master data management server, ibm omnifind, ibm industry data models, ibm balanced warehouses, oltp-based transactional data
    
IBM
Published By: IBM     Published Date: Jun 15, 2009
The ability to make quick, well-informed decisions is critical to competitiveness and growth for most companies. Read the white paper to see how Data warehouse solutions can deliver business insight across virtually any business process or function. And also how they're particularly valuable for understanding sales, profiling customers and analyzing business costs.
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ibm, data warehouses, warehouse, data, data solutions, sales, business costs, olap, online, analytical processing, customer relationship management, crm, ibm db2 warehouse, regulatory, compliance
    
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: Oct 06, 2014
Business Intelligence (BI) has become a mandatory part of every enterprise’s decision-making fabric. Unfortunately in many cases, with this rise in popularity, came a significant and disturbing complexity. Many BI environments began to have a myriad of moving parts: data warehouses and data marts deployed on multiple platforms and technologies – each requiring significant effort to ensure performance and support for the various needs and skill sets of the business resources using the environment. These convoluted systems became hard to manage or enhance with new requirements. To remain viable and sustainable, they must be simplified. Fortunately today, we have the ability to build simpler BI technical environments that still support the necessary business requirements but without the ensuing management complexity. This paper covers what is needed to simplify BI environments and the technologies that support this simplification.
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data warehouses, bi environments, bi technologies, faster deployments
    
IBM
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