data warehouse

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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: IBM     Published Date: Nov 08, 2017
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, ibm, deployment models
    
IBM
Published By: Oracle     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
Published By: Oracle     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
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
Published By: IBM APAC     Published Date: Aug 25, 2017
The world of business analytics is evolving rapidly, and while there are multiple emerging trends of note, two stand out as particularly impactful. First, there is an expanding and increasingly diverse audience of users that are becoming more analytically active. From mid-level Line-of-Business staff to senior executives on mahogany row, more users in more job functions are taking an increased level of ownership in the insight that fuels their decisions and the underlying data that supports that insight.
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data integration, data security, data optimization, data virtualization, database security
    
IBM APAC
Published By: CA Technologies EMEA     Published Date: Aug 03, 2017
Tenuto conto del fatto che la GDPR è stato annunciato formalmente solo di recente, si evidenzia un buon livello di consapevolezza tra i partecipanti. Una volta informati sul regolamento, l'88% degli intervistati ha dichiarato che la propria azienda deve affrontare difficoltà tecnologiche per la compliance alla GDPR. Il percorso verso la compliance è percepito come molto laborioso.
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generazione di dati sintetici, virtualizzazione di servizi, controllo e protezione dei database, masking dei dati, warehouse dei dati, ca technologies, gdpr
    
CA Technologies EMEA
Published By: Adobe     Published Date: Aug 02, 2017
With Adobe Analytics and Adobe Audience Manager—both part of Adobe Marketing Cloud—media companies can overcome today’s audience intelligence challenges. Adobe Analytics is an industry-leading solution for applying real time analytics and detailed segmentation across all of your marketing channels. A unified platform and customer ID unlock powerful customer intelligence and help you discover and retain high-value audiences. Make forward-looking decisions with its predictive intelligence capabilities, and find out which of your marketing efforts are paying off with its attribution functionality.
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goals and kpis, data warehouse, resource management, insight loop, content and campaigns, audience optimisation
    
Adobe
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: Pure Storage Australia     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
    
Pure Storage Australia
Published By: Zebra Technologies     Published Date: Jun 21, 2017
Best practices for integrating mobile, wireless and data capture technologies into warehouse management. Download now!
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Zebra Technologies
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