This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
This article takes a look at some of the reasons behind this data explosion, and some of the possible effects if the growth is not managed. We’ll also examine some of the ways in which these problems can be avoided.
The document discusses six reasons why colocation makes better business sense than building an internal data center. It notes that colocation provides better use of capital by avoiding large upfront costs, allows for high availability through redundant infrastructure, and increases focus on innovation by reducing time spent on unexpected IT issues. Colocation also enables lower energy costs through efficient data center design and a greener approach to IT.
Organizations are facing rapidly growing data due to increased data collection and virtualization. This growth strains backup and storage capabilities. IBM Real-time Compression Appliances can reduce primary and backup data by up to 80% in real-time without performance impacts, addressing storage efficiency issues and reducing costs. At Ben-Gurion University, real-time compression provided 65-83% reductions, extending infrastructure lifespan.
Analytics 3.0 represents a new approach that combines traditional analytics (Analytics 1.0) with big data analytics (Analytics 2.0). It allows organizations to rapidly deliver insights that provide business impact. Key characteristics include analytics being integral to running the business as a strategic asset, rapid and agile delivery of insights, and cultural changes that embed analytics in decision-making. This new approach allows any organization in any industry to participate in the data economy by developing data-based products and services.
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
Go Green to Save Green – Embracing Green Energy PracticesLindaWatson19
Green is not just media/technology hype. IT organizations can reduce their carbon footprint, reduce energy consumption and drive cost out of the data center. This paper examines the costs and strategies that can be deployed to reduce Tier 1 storage in production and reduce the overall storage and servers required for data management.
The document discusses how hybrid IT infrastructure solutions, which utilize a mix of colocated data centers, managed services, and cloud computing, allow organizations to balance IT agility demands with cost constraints. It notes that a recent survey found most companies will rely on a hybrid model for the next 5 years. The hybrid approach allows companies to select the right infrastructure type for each application based on factors like risk, cost, and agility needs. Colocation is often the initial step as it provides control and quick deployment, while managed services and cloud use will grow over time.
Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
This article takes a look at some of the reasons behind this data explosion, and some of the possible effects if the growth is not managed. We’ll also examine some of the ways in which these problems can be avoided.
The document discusses six reasons why colocation makes better business sense than building an internal data center. It notes that colocation provides better use of capital by avoiding large upfront costs, allows for high availability through redundant infrastructure, and increases focus on innovation by reducing time spent on unexpected IT issues. Colocation also enables lower energy costs through efficient data center design and a greener approach to IT.
Organizations are facing rapidly growing data due to increased data collection and virtualization. This growth strains backup and storage capabilities. IBM Real-time Compression Appliances can reduce primary and backup data by up to 80% in real-time without performance impacts, addressing storage efficiency issues and reducing costs. At Ben-Gurion University, real-time compression provided 65-83% reductions, extending infrastructure lifespan.
Analytics 3.0 represents a new approach that combines traditional analytics (Analytics 1.0) with big data analytics (Analytics 2.0). It allows organizations to rapidly deliver insights that provide business impact. Key characteristics include analytics being integral to running the business as a strategic asset, rapid and agile delivery of insights, and cultural changes that embed analytics in decision-making. This new approach allows any organization in any industry to participate in the data economy by developing data-based products and services.
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
Go Green to Save Green – Embracing Green Energy PracticesLindaWatson19
Green is not just media/technology hype. IT organizations can reduce their carbon footprint, reduce energy consumption and drive cost out of the data center. This paper examines the costs and strategies that can be deployed to reduce Tier 1 storage in production and reduce the overall storage and servers required for data management.
The document discusses how hybrid IT infrastructure solutions, which utilize a mix of colocated data centers, managed services, and cloud computing, allow organizations to balance IT agility demands with cost constraints. It notes that a recent survey found most companies will rely on a hybrid model for the next 5 years. The hybrid approach allows companies to select the right infrastructure type for each application based on factors like risk, cost, and agility needs. Colocation is often the initial step as it provides control and quick deployment, while managed services and cloud use will grow over time.
Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
Msft Top10 Business Practicesfor Es Data Centers April09hutuworm
Microsoft outlines its top 10 business practices for environmentally sustainable data centers. These practices include providing incentives for improving efficiency metrics like PUE; focusing on effective utilization of resources rather than just capacity; using virtualization to improve server utilization; driving quality through compliance with standards; embracing change management processes; understanding application workloads; right-sizing server platforms; testing servers' performance and energy efficiency; converging on a small number of standard server models; and fostering innovation through competitive bids from manufacturers. Implementing these practices can help reduce energy use, waste, and costs while increasing efficiency and returns on investment.
This document provides an overview and update on Attestor 2.0. It discusses the migration plan for current Attestor customers to Attestor 2.0 which will have improved functionality while leveraging the power of PrivacyCentral. Attestor 2.0 will allow for custom organization hierarchies, accountability mechanisms, and automated question flows. It will also provide interactive reporting and reduce redundant questions. Customers can expect support from their customer success manager during the migration and for Attestor 2.0 to significantly reduce manual work compared to the previous version.
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
BRIDGING DATA SILOS USING BIG DATA INTEGRATIONijmnct
1) The document discusses how big data integration can be used to bridge data silos that exist in many enterprises due to different business applications generating structured, semi-structured, and unstructured data. 2) It explains that traditional data integration techniques are not well-suited for big data due to issues with scale and handling semi-structured and unstructured data. 3) Big data integration techniques like Hadoop, Spark, Kafka and data lakes can be better suited for integrating large heterogeneous data sources in real-time or in batches at scale.
The document discusses trends and strategies around green data centers. It outlines policies promoting green data centers in the US, Europe and Asia. Current greening statistics show trends around server virtualization, power monitoring and recycling hardware. Power consumption is a growing concern as data center density increases. The US is aiming to reduce energy intensity through initiatives like Save Energy Now. New provisioning strategies aim to optimize capacity, instrumentation and efficiency. Creating durable data centers requires focusing on context, relationships and maximizing value over time.
Big data refers to extremely large data sets that traditional data processing systems cannot handle. Big data is characterized by high volume, velocity, and variety of data. Hadoop is an open-source software framework that allows distributed storage and processing of big data across clusters of computers. A key component of Hadoop is MapReduce, a programming model that enables parallel processing of large datasets. MapReduce allows programmers to break problems into independent pieces that can be processed simultaneously across distributed systems.
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...IRJET Journal
This document discusses big data analytical methods, cloud computing, and how they can be combined. It explains that big data involves large amounts of structured, semi-structured, and unstructured data from various sources that requires significant computing resources to analyze. Cloud computing provides a way for big data analytics to be offered as a service and processed efficiently using cloud resources. The integration of big data and cloud computing allows organizations to gain business intelligence from large datasets in a flexible, scalable and cost-effective manner.
This document outlines the top 10 big data security and privacy challenges as identified by the Cloud Security Alliance. It discusses each challenge in terms of use cases. The challenges are: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transaction logs, 4) end-point input validation/filtering, 5) real-time security/compliance monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced access control and secure communication, 8) granular access control, 9) granular audits, and 10) data provenance. Each challenge is described briefly and accompanied by example use cases.
This document summarizes key findings from a survey of 200 IT professionals about big data analytics. The main findings are:
- Big data and data center infrastructure updates are the top strategic priorities for IT managers. Big data is the number one priority for 21% of respondents.
- Most organizations already have a formal big data analytics strategy in place or plan to have one within the next six months. The majority will have a strategy within a year.
- Over half of respondents have already deployed or are currently implementing the Apache Hadoop framework. Half of those use an internal private cloud.
- The leading current uses of big data relate to understanding staffing levels and productivity, and generating competitive intelligence. Future uses
Originally Published: Jan 21, 2015
The size and complexity of data make it difficult for companies to unlock the true value of their data. IBM Information Integration Governance can improve data quality, protect sensitive data, and reduce cost and risk. Free up your resources and get more out of your data.
IRJET- Analysis of Big Data Technology and its ChallengesIRJET Journal
This document discusses big data technology and its challenges. It begins by defining big data as large, complex data sets that are growing exponentially due to increased internet usage and digital data collection. It then outlines the key steps in big data analysis: acquisition, assembly, analysis, and action. Several big data technologies that support these steps are described, including Hadoop, MapReduce, Pig, and Hive. The document concludes by examining challenges of big data such as storage limitations, data lifecycle management, analysis difficulties, and ensuring data privacy and security when analyzing large datasets.
This document summarizes an IDC white paper about IBM's Managed Technical Support and Lifecycle Maintenance service. It finds that IT organizations spend significant time on maintenance tasks that take away from innovation. IBM's service aims to streamline asset management, support, and refreshes through a flexible opex model. The service provides multivendor support, reduces IT complexity, and allows organizations to focus on business objectives rather than maintenance.
Navigating Storage in a Cloudy EnvironmentHGST Storage
Steve Campbell, Chief Technology Officer at HGST, presents at Cloud Expo 2013 on the data center evolution, enterprise solid state drives, the future of data storage, and more.
Lighten Your Data Center TCO With “Helium” Storage SolutionsHGST Storage
HGST introduces its new HelioSeal technology platform for hard disk drives. HelioSeal uses helium instead of air inside drives to allow for higher capacities, lower power consumption, and cooler operation. As the first product using this technology, HGST announces the Ultrastar He6, a 6TB 3.5-inch hard drive that offers over 50% more capacity than comparable air-filled drives while reducing power usage by 23%. HelioSeal addresses challenges around continued areal density growth and the increasing costs of data centers.
A technical Introduction to Big Data AnalyticsPethuru Raj PhD
This presentation gives the details about the sources for big data, the value of big data, what to do with big data, the platforms, the infrastructures and the architectures for big data analytics
- IBM offers a Managed Technical Support and Lifecycle Maintenance service to help organizations reduce IT complexity and costs associated with maintaining aging assets through an operating expenditure model rather than capital expenditure.
- The service provides multivendor support, asset management including refresh, and acts as a single point of contact to simplify support. This allows IT staff to focus on innovation rather than day-to-day maintenance tasks.
- Adopting an "as-a-service" model through IBM's solution can help organizations better manage technologies and shift costs from capital to operating expenditures in a flexible way.
IDC Study on Enterprise Hybrid Cloud StrategiesEMC
White Paper discussing IDC Survey of over 650 enterprise IT decision makers that was designed to understand the evolution of the cloud across world’s largest IT organizations.
Enterprise Information Management (EIM) is an organizational commitment to define, secure, and improve information accuracy across boundaries to support business objectives. There are 7 key trends in EIM: 1) Focus is shifting from control to access and prioritizing people over content. 2) Keyword search is no longer effective and alternatives like machine learning are needed. 3) Systems are becoming more complex while users prefer simplicity. 4) Ease of use, implementation, and integration are important. 5) SharePoint focuses too much on content before people. 6) Communication tools are still used more than dedicated collaboration systems. 7) Information architecture models need to evolve to better solve problems. Research shows user adoption and experience are most critical for technology success
Whether due to disaster recovery, business continuity, or regulatory compliance needs, data backup and recovery plays a critical role for enterprises in India. Many large IT companies offer a wide range of data backup and recovery systems and solutions. The growing market has also benefited channel partners and specialist solution providers. While tape storage remains useful for archiving, disk-based backup systems are becoming more widely adopted due to lower costs, faster backup and recovery times, and the ability to handle growing data volumes and mission critical applications. The emergence of cloud computing is also impacting how enterprises approach data backup and recovery.
The document discusses the major determinants of supply and how they can cause the supply curve to shift. It identifies four major determinants: input prices, resources, wages, and taxes/regulations. It explains how changes in these determinants, such as a decrease in the price of inputs, can lead producers to supply more at a lower cost, shifting the supply curve to the right. The document also discusses how expectations of future prices, number of firms in an industry, and technology can all impact the supply curve.
The document discusses how the Industrial Internet will transform the way people work by empowering them with faster access to relevant information and better tools for collaboration. It will allow workers like field engineers, pilots, and medical professionals to make data-driven decisions that reduce downtime of equipment and optimize operations. The Industrial Internet connects machines, analytics, and people, making information intelligent and available to workers on mobile devices. This will make work more efficient and productive while enabling workers to spend more time on higher-value tasks and upgrade their skills. While technology is often seen as a threat, the Industrial Internet will augment workers' abilities rather than replace them.
Msft Top10 Business Practicesfor Es Data Centers April09hutuworm
Microsoft outlines its top 10 business practices for environmentally sustainable data centers. These practices include providing incentives for improving efficiency metrics like PUE; focusing on effective utilization of resources rather than just capacity; using virtualization to improve server utilization; driving quality through compliance with standards; embracing change management processes; understanding application workloads; right-sizing server platforms; testing servers' performance and energy efficiency; converging on a small number of standard server models; and fostering innovation through competitive bids from manufacturers. Implementing these practices can help reduce energy use, waste, and costs while increasing efficiency and returns on investment.
This document provides an overview and update on Attestor 2.0. It discusses the migration plan for current Attestor customers to Attestor 2.0 which will have improved functionality while leveraging the power of PrivacyCentral. Attestor 2.0 will allow for custom organization hierarchies, accountability mechanisms, and automated question flows. It will also provide interactive reporting and reduce redundant questions. Customers can expect support from their customer success manager during the migration and for Attestor 2.0 to significantly reduce manual work compared to the previous version.
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
BRIDGING DATA SILOS USING BIG DATA INTEGRATIONijmnct
1) The document discusses how big data integration can be used to bridge data silos that exist in many enterprises due to different business applications generating structured, semi-structured, and unstructured data. 2) It explains that traditional data integration techniques are not well-suited for big data due to issues with scale and handling semi-structured and unstructured data. 3) Big data integration techniques like Hadoop, Spark, Kafka and data lakes can be better suited for integrating large heterogeneous data sources in real-time or in batches at scale.
The document discusses trends and strategies around green data centers. It outlines policies promoting green data centers in the US, Europe and Asia. Current greening statistics show trends around server virtualization, power monitoring and recycling hardware. Power consumption is a growing concern as data center density increases. The US is aiming to reduce energy intensity through initiatives like Save Energy Now. New provisioning strategies aim to optimize capacity, instrumentation and efficiency. Creating durable data centers requires focusing on context, relationships and maximizing value over time.
Big data refers to extremely large data sets that traditional data processing systems cannot handle. Big data is characterized by high volume, velocity, and variety of data. Hadoop is an open-source software framework that allows distributed storage and processing of big data across clusters of computers. A key component of Hadoop is MapReduce, a programming model that enables parallel processing of large datasets. MapReduce allows programmers to break problems into independent pieces that can be processed simultaneously across distributed systems.
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...IRJET Journal
This document discusses big data analytical methods, cloud computing, and how they can be combined. It explains that big data involves large amounts of structured, semi-structured, and unstructured data from various sources that requires significant computing resources to analyze. Cloud computing provides a way for big data analytics to be offered as a service and processed efficiently using cloud resources. The integration of big data and cloud computing allows organizations to gain business intelligence from large datasets in a flexible, scalable and cost-effective manner.
This document outlines the top 10 big data security and privacy challenges as identified by the Cloud Security Alliance. It discusses each challenge in terms of use cases. The challenges are: 1) secure computations in distributed programming frameworks, 2) security best practices for non-relational data stores, 3) secure data storage and transaction logs, 4) end-point input validation/filtering, 5) real-time security/compliance monitoring, 6) scalable and composable privacy-preserving data mining and analytics, 7) cryptographically enforced access control and secure communication, 8) granular access control, 9) granular audits, and 10) data provenance. Each challenge is described briefly and accompanied by example use cases.
This document summarizes key findings from a survey of 200 IT professionals about big data analytics. The main findings are:
- Big data and data center infrastructure updates are the top strategic priorities for IT managers. Big data is the number one priority for 21% of respondents.
- Most organizations already have a formal big data analytics strategy in place or plan to have one within the next six months. The majority will have a strategy within a year.
- Over half of respondents have already deployed or are currently implementing the Apache Hadoop framework. Half of those use an internal private cloud.
- The leading current uses of big data relate to understanding staffing levels and productivity, and generating competitive intelligence. Future uses
Originally Published: Jan 21, 2015
The size and complexity of data make it difficult for companies to unlock the true value of their data. IBM Information Integration Governance can improve data quality, protect sensitive data, and reduce cost and risk. Free up your resources and get more out of your data.
IRJET- Analysis of Big Data Technology and its ChallengesIRJET Journal
This document discusses big data technology and its challenges. It begins by defining big data as large, complex data sets that are growing exponentially due to increased internet usage and digital data collection. It then outlines the key steps in big data analysis: acquisition, assembly, analysis, and action. Several big data technologies that support these steps are described, including Hadoop, MapReduce, Pig, and Hive. The document concludes by examining challenges of big data such as storage limitations, data lifecycle management, analysis difficulties, and ensuring data privacy and security when analyzing large datasets.
This document summarizes an IDC white paper about IBM's Managed Technical Support and Lifecycle Maintenance service. It finds that IT organizations spend significant time on maintenance tasks that take away from innovation. IBM's service aims to streamline asset management, support, and refreshes through a flexible opex model. The service provides multivendor support, reduces IT complexity, and allows organizations to focus on business objectives rather than maintenance.
Navigating Storage in a Cloudy EnvironmentHGST Storage
Steve Campbell, Chief Technology Officer at HGST, presents at Cloud Expo 2013 on the data center evolution, enterprise solid state drives, the future of data storage, and more.
Lighten Your Data Center TCO With “Helium” Storage SolutionsHGST Storage
HGST introduces its new HelioSeal technology platform for hard disk drives. HelioSeal uses helium instead of air inside drives to allow for higher capacities, lower power consumption, and cooler operation. As the first product using this technology, HGST announces the Ultrastar He6, a 6TB 3.5-inch hard drive that offers over 50% more capacity than comparable air-filled drives while reducing power usage by 23%. HelioSeal addresses challenges around continued areal density growth and the increasing costs of data centers.
A technical Introduction to Big Data AnalyticsPethuru Raj PhD
This presentation gives the details about the sources for big data, the value of big data, what to do with big data, the platforms, the infrastructures and the architectures for big data analytics
- IBM offers a Managed Technical Support and Lifecycle Maintenance service to help organizations reduce IT complexity and costs associated with maintaining aging assets through an operating expenditure model rather than capital expenditure.
- The service provides multivendor support, asset management including refresh, and acts as a single point of contact to simplify support. This allows IT staff to focus on innovation rather than day-to-day maintenance tasks.
- Adopting an "as-a-service" model through IBM's solution can help organizations better manage technologies and shift costs from capital to operating expenditures in a flexible way.
IDC Study on Enterprise Hybrid Cloud StrategiesEMC
White Paper discussing IDC Survey of over 650 enterprise IT decision makers that was designed to understand the evolution of the cloud across world’s largest IT organizations.
Enterprise Information Management (EIM) is an organizational commitment to define, secure, and improve information accuracy across boundaries to support business objectives. There are 7 key trends in EIM: 1) Focus is shifting from control to access and prioritizing people over content. 2) Keyword search is no longer effective and alternatives like machine learning are needed. 3) Systems are becoming more complex while users prefer simplicity. 4) Ease of use, implementation, and integration are important. 5) SharePoint focuses too much on content before people. 6) Communication tools are still used more than dedicated collaboration systems. 7) Information architecture models need to evolve to better solve problems. Research shows user adoption and experience are most critical for technology success
Whether due to disaster recovery, business continuity, or regulatory compliance needs, data backup and recovery plays a critical role for enterprises in India. Many large IT companies offer a wide range of data backup and recovery systems and solutions. The growing market has also benefited channel partners and specialist solution providers. While tape storage remains useful for archiving, disk-based backup systems are becoming more widely adopted due to lower costs, faster backup and recovery times, and the ability to handle growing data volumes and mission critical applications. The emergence of cloud computing is also impacting how enterprises approach data backup and recovery.
The document discusses the major determinants of supply and how they can cause the supply curve to shift. It identifies four major determinants: input prices, resources, wages, and taxes/regulations. It explains how changes in these determinants, such as a decrease in the price of inputs, can lead producers to supply more at a lower cost, shifting the supply curve to the right. The document also discusses how expectations of future prices, number of firms in an industry, and technology can all impact the supply curve.
The document discusses how the Industrial Internet will transform the way people work by empowering them with faster access to relevant information and better tools for collaboration. It will allow workers like field engineers, pilots, and medical professionals to make data-driven decisions that reduce downtime of equipment and optimize operations. The Industrial Internet connects machines, analytics, and people, making information intelligent and available to workers on mobile devices. This will make work more efficient and productive while enabling workers to spend more time on higher-value tasks and upgrade their skills. While technology is often seen as a threat, the Industrial Internet will augment workers' abilities rather than replace them.
This document contains instructions for a Latin American history bellringer activity. Students are asked to describe the region of Latin America, analyze a magazine cover using OPTIC, and discuss issues facing Latin America today and how it has been shaped by historical events studied. Students are then instructed to create a map of Europe labeling important scientists and discoveries from Chapter 17 and rank those discoveries by their impact on life today. Finally, students are to make notes on Latin American history using terms, people, and events provided in a list to create a detailed map, connected web, or timeline in preparation for a final exam.
The document discusses when Spanish was first spoken in Mexico, describes the Mexican flag, and prompts discussion questions about taking things that belong to others and examples. It also mentions Aztecs, Tenochtitlan, and includes instructions to draw and label a map of the New World in the 1700s.
This document is a bellringer activity for a history class that provides announcements and instructions for students. It asks students to consider why the Mayan culture collapsed, identifies a Roman coin as a primary source, and directs students to read about theories for why the Roman Empire fell and summarize them in their notes. Students are asked to identify the most plausible theory and which two theories would be most and least likely to happen to the modern US.
PyCon lightning talk on my Toro module for Tornadoemptysquare
With Tornado’s gen module, you can turn Python generators into full-featured coroutines, but coordination among these coroutines is difficult without mutexes, semaphores, and queues.
Toro provides to Tornado coroutines a set of locking primitives and queues analogous to those that Gevent provides to Greenlets, or that the standard library provides to threads.
This document discusses key concepts around perfect competition, including:
- Perfect competition is defined by many small firms, identical products, firms as price takers, easy entry and exit into the market, and perfectly informed consumers.
- Examples of perfect competition include commodities like salt, gasoline, and paper clips.
- Under perfect competition, a firm's marginal revenue is equal to the market price since each individual firm is such a small part of the market that it cannot influence price.
The document discusses the Texas STaR Chart, a teacher self-assessment tool aligned with the state's long-range technology plan. It analyzes the school's ratings across the STaR Chart's four key areas: Teaching and Learning scored lowest at 9 out of 24; Educator Preparation and Development scored 13 out of 24; Leadership, Administration and Instructional Support scored 11 out of 24; and Infrastructure for Technology scored 14 out of 24. All areas were rated "Developing Tech." The document concludes that the school needs to continue improving across all STaR Chart areas and put more resources toward Teaching and Learning, its lowest rated section.
This document contains notes from a history class discussing the Medici family of Florence during the Renaissance. It includes discussion prompts about a painting by Giorgione and successful versus unsuccessful families. There are also notes on the Medici family specifically, how they ran Florence and supported Renaissance artists, as well as an assignment involving an illustrated timeline and questions about the Medici family's contributions and support of education.
This document discusses externalities, which are side effects of economic activity that impact third parties. It provides examples of negative externalities like pollution and examples of positive externalities like education. It explains how negative externalities can lead to market failures as the social costs exceed the private costs. The document uses diagrams to illustrate how a tax can correct for a negative externality from gasoline consumption and how a subsidy can encourage a positive externality from flu shots. It also provides examples of policy options like cap-and-trade programs to reduce pollution at lower costs than traditional regulation.
All of material inside is un-licence, kindly use it for educational only but please do not to commercialize it.
Based on 'ilman nafi'an, hopefully this file beneficially for you.
Thank you.
This document provides an overview and lesson plan for a class on the law of demand and consumer surplus. It includes:
1) An introduction to the topic of the day - the law of demand and why it is important to understand consumer demand.
2) A definition of demand as the amount willing and able to be bought at various prices.
3) Instructions to create a demand schedule and supply/demand graph with proper labels as an example.
4) A definition of the law of demand that as price decreases, quantity demanded increases, and vice versa.
5) An explanation that changes in price do not actually change demand but rather the quantity demanded at the margin.
6) A
This document contains notes and instructions for students on the topics of types of labor, minimum wage, and price floors/ceilings. It includes discussion questions, assignments on taking Cornell notes on price ceilings/floors, instructions for an in-class debate between groups arguing for and against raising the minimum wage or implementing various price floors, and requirements for a 4 paragraph essay if a student misses the debate discussion in class.
The document provides questions about currency figures on US bills and a 20 Yuan Chinese note, and asks students to identify six key events from a timeline or comic that show how China became communist under Mao Zedong's leadership, with the events to be listed in order and how they helped Mao and the Chinese Communist Party win control of China.
1. The document contains a short quiz about types of unemployment, calculating unemployment rates, GDP, and defining aspects of money and currency.
2. It asks the reader to identify types of unemployment, calculate unemployment rates and GDP for a country called Travistan from data provided, and define key aspects of money.
3. The second half of the document provides background information on Zimbabwe's economy and asks the reader to advise the president on how to improve the struggling economy.
All of material inside is un-licence, kindly use it for educational only but please do not to commercialize it.
Based on 'ilman nafi'an, hopefully this file beneficially for you.
Thank you.
An elderly Chinese woman carries two large pots on a pole across her neck to collect water from a stream every day. One pot has a crack and only arrives home half full, while the other pot is perfect and always full. After two years, the cracked pot expresses shame to the woman for only being able to complete half its task. However, the woman reveals that she planted flowers along the path of the cracked pot, which it has watered each day on the long walk home. The moral is that our flaws allow for beauty and make life more interesting.
Here are the answers to the short quiz:
1. Examples of FC for this firm: Rent for factory space, machinery/equipment, salaries for managers
2. Examples of VC for this firm: Materials/supplies, labor costs for production workers, electricity
3. This firm could increase its TR by increasing the quantity of widgets produced and sold (assuming demand remains constant)
4. An example of diminishing marginal returns for this firm would be if each additional widget took longer to produce due to congestion in the factory or workers getting tired, so the marginal cost of production would increase with higher quantities.
Pdf wp-emc-mozyenterprise-hybrid-cloud-backuplverb
This document discusses hybrid backup architectures that use both on-premises and cloud-based technologies for data protection. A hybrid approach protects data in the data center locally but also uses the cloud to back up data from remote offices and mobile devices. This provides comprehensive data protection while reducing management burdens. The document recommends looking for a hybrid solution that ensures recoverability, is manageable by IT, supports remote workers, and increases productivity through secure access to files from any device.
Enterprises are facing exponentially increasing amounts of data that is breaking down traditional storage architectures. NetApp addresses this "big data challenge" through their "Big Data ABCs" approach - focusing on analytics, bandwidth, and content. This enables customers to gain insights from massive datasets, move data quickly for high-speed applications, and securely store unlimited amounts of content for long periods without increasing complexity. NetApp's solutions provide a foundation for enterprises to innovate with data and drive business value.
A Data-driven Maturity Model for Modernized, Automated, and Transformed ITbalejandre
This document presents a research-based maturity model for measuring organizations' progress in IT transformation. The model segments organizations into four levels of maturity based on surveys of 1,000 IT executives about their infrastructure, processes, and relationships. Only a small percentage have achieved the highest levels of modernized infrastructure, automated processes, and business-IT alignment needed for digital transformation. Higher maturity is correlated with improved agility, efficiency, innovation funding, and business outcomes. Adopting modern data center technologies, automated processes, and DevOps practices can help organizations progress to more mature states.
How Analytics Has Changed in the Last 10 Years (and How It’s Staye.docxpooleavelina
How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same)
· Thomas H. Davenport
June 22, 2017
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Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics.
Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade.
The last decade, of course, was the era of big data. New data sources such as online clickstreams required a variety of new hardware offerings on premise and in the cloud, primarily involving distributed computing — spreading analytical calculations across multiple commodity servers — or specialized data appliances. Such machines often analyze data “in memory,” which can dramatically accelerate times-to-answer. Cloud-based analytics made it possible for organizations to acquire massive amounts of computing power for short periods at low cost. Even small businesses could get in on the act, and big companies began using these tools not just for big data but also for traditional small, structured data.
Insight Center
· Putting Data to Work
Analytics are critical to companies’ performance.
Along with the hardware advances, the need to store and process big data in new ways led to a whole constellation of open source software, such as Hadoop and scripting languages. Hadoop is used to store and do basic processing on big data, and it’s typically more than an order of magnitude cheaper than a data warehouse for similar volumes of data. Today many organizations are employing Hadoop-based data lakes to store different types of data in their original formats until they need to be structured and analyzed.
Since much of big data is relatively unstructured, data scientists created ways to make it structured and ready for statistical analysis, with new (and old) scripting languages like Pig, Hive, and Python. More-specialized open source tools, such as Spark for streaming data and R for statistics, have also gained substantial popularity. The process of acquiring and using open source software is a major change in itself for established busines ...
Nuestar "Big Data Cloud" Major Data Center Technology nuestarmobilemarketing...IT Support Engineer
Nuestar Communications provides big data and cloud technology solutions to help organizations analyze large datasets and extract value from data. Their platform allows for tightly coupled data integration across various data sources and analytics to support the entire big data lifecycle. Nuestar helps clients address challenges around managing large and varied data, determining what data is most important, and using all of their data to make better decisions.
The white paper discusses how enterprises are facing exponentially growing amounts of data that is breaking down traditional storage architectures. It outlines NetApp's approach to addressing big data challenges through what it calls the "Big Data ABCs" - analytics, bandwidth, and content. This allows customers to gain insights from massive data sets, move data quickly for high-performance applications, and store large amounts of content for long periods without increasing complexity. NetApp provides solutions to help enterprises take advantage of big data and turn it into business value.
The document discusses how utilities are increasingly collecting and generating large amounts of data from smart meters and other sensors. It notes that utilities must learn to leverage this "big data" by acquiring, organizing, and analyzing different types of structured and unstructured data from various sources in order to make more informed operational and business decisions. Effective use of big data can help utilities optimize operations, improve customer experience, and increase business performance. However, most utilities currently underutilize data analytics capabilities and face challenges in integrating diverse data sources and systems. The document advocates for a well-designed data management platform that can consolidate utility data to facilitate deeper analysis and more valuable insights.
Here are the answers to the assignment questions:
1. Big data refers to huge volumes of both structured and unstructured data that is so large in size and complex that traditional data processing applications are inadequate to deal with it.
2. The three main types of data are:
- Structured data: Data that is organized and has a predefined data model e.g. numbers in a database. Sources include CRM systems, transactions etc.
- Semi-structured data: Data that has some structure but not fully structured e.g. log files, XML files. Sources include sensors, images, audio/video etc.
- Unstructured data: Data with no predefined structure e.g. text, emails. Sources include
Build a Winning Data Strategy in 2022.pdfAvinashBatham
Tredence is a leader in advanced analytics and full-stack AI services,
recognized as a Forrester Wave Leader in Customer Analytics in 2021 Q3 and the AI Gamechanger by NASSCOM.
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9 Steps to Successful Information Lifecycle Management: Best Practices for Efficient Database Archiving
Executive Summary
Organizations that use prepackaged ERP/CRM, custom, and third-party applications are seeing their production databases grow exponentially. At the same time, business policies and regulations require them to retain structured and unstructured data indefinitely. Storing increasing amounts of data on production systems is a recipe for poor performance no matter how much hardware is added or how much an application is tuned. Organizations need a way to manage this growth effectively.
Over the past few years, the Storage Networking Industry Association (SNIA) has promoted the concept of Information Lifecycle Management (ILM) as a means of better aligning the business value of data with the most appropriate and cost-effective IT infrastructure—from the time information is added to the database until it can be destroyed. However, the SNIA does not recommend specifi c tools to get the job done or how best to use tools to implement ILM.
This white paper describes why data archiving provides a highly effective application ILM solution and how to implement such an archiving solution to most effectively manage data throughout its
life cycle.
This document provides an overview of big data adoption based on a survey of 255 professionals. Key findings include:
1) Big data has evolved from a focus on size to prioritizing data structure, processing speed, and extracting business value.
2) Companies now manage big data across a hybrid ecosystem of platforms like Hadoop and data warehouses, rather than a single centralized system. This allows aligning different data types and workloads to the best suited platform.
3) Adoption of big data is growing, with over half of companies having ongoing big data programs. The most common initial uses are in marketing, fraud detection, and IT operations. Implementation challenges include integrating diverse data and a lack of skills.
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
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Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
The Big Data Importance – Tools and their UsageIRJET Journal
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LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
This document discusses data warehousing and data mining. It defines data warehousing as the process of centralizing data from different sources for analysis. Data mining is described as the process of analyzing data to uncover hidden patterns and relationships. The document provides examples of how data mining and data warehousing can be used together, with data warehousing collecting and organizing data that is then analyzed using data mining techniques to generate useful insights. Applications of data mining and data warehousing discussed include medicine, finance, marketing, and scientific discovery.
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