IBM's "Smarter Storage" initiative aims to make storage infrastructure more intelligent through three pillars: efficient by design to manage costs and capacity growth, self-optimizing for improved performance and productivity, and cloud agile to use storage effectively in cloud models. The document discusses each pillar in detail, emphasizing that Smarter Storage takes a data-centric rather than media-centric view and blurs the lines between storage and data management to provide a range of smarter storage services.
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 where finance and IT meet and how they can better work together. It proposes three models: 1) increasing cross-learning between finance and IT teams, 2) specializing knowledge but coordinating at senior levels, or 3) a mix of cross-learning foundational knowledge with specialization. The takeaways are that finance needs a common IT language and knowledge base to understand technology potential and work with heterogeneous, integrated systems delivering business needs. The information manager coordinates initially, but finance roles like controllers require significantly more IT literacy.
The document summarizes and compares IBM and EMC's strategies for information infrastructure. It finds that IBM takes a more holistic, solution-oriented approach to address all customer needs, while EMC maintains a stronger product focus through its disk, security, and content management business units. The document also notes that IBM can provide a more complete set of hardware, software, services and financing to support customers' information infrastructure transformations.
The cumulative effect of decades of IT infrastructure investment around a diverse set of technologies and processes has stifled innovation at organizations around the globe. Layer upon layer of complexity to accommodate a staggering array of applications has created hardened processes that make changes to systems difficult and cumbersome.
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document discusses the future of information management over the next 10 years. It notes that data is growing exponentially and will continue to do so. It identifies key requirements for data management like scalability, availability, security, and productivity. It then highlights how IBM's DB2 database on the System z mainframe platform meets these requirements through capabilities like industry-leading performance, security, workload consolidation, and reduced costs. The document concludes by discussing IBM's vision for harnessing big data through smart data analytics to help organizations make better, faster decisions.
This document discusses how hyperscale infrastructure approaches can enable enterprises to meet increasing future IT capacity needs with lower costs than traditional IT approaches. It describes how leading cloud providers have developed hyperscale computing models internally to dramatically improve efficiency and performance. The document proposes that operators and enterprises can adopt similar hyperscale infrastructure using disaggregated hardware architectures, which standardize components, abstract complexity, automate processes, and allow perpetual refresh of parts rather than entire systems. This would enable lower total cost of ownership through improvements like high utilization rates, reduced energy consumption, and eliminating forced hardware replacement cycles.
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 where finance and IT meet and how they can better work together. It proposes three models: 1) increasing cross-learning between finance and IT teams, 2) specializing knowledge but coordinating at senior levels, or 3) a mix of cross-learning foundational knowledge with specialization. The takeaways are that finance needs a common IT language and knowledge base to understand technology potential and work with heterogeneous, integrated systems delivering business needs. The information manager coordinates initially, but finance roles like controllers require significantly more IT literacy.
The document summarizes and compares IBM and EMC's strategies for information infrastructure. It finds that IBM takes a more holistic, solution-oriented approach to address all customer needs, while EMC maintains a stronger product focus through its disk, security, and content management business units. The document also notes that IBM can provide a more complete set of hardware, software, services and financing to support customers' information infrastructure transformations.
The cumulative effect of decades of IT infrastructure investment around a diverse set of technologies and processes has stifled innovation at organizations around the globe. Layer upon layer of complexity to accommodate a staggering array of applications has created hardened processes that make changes to systems difficult and cumbersome.
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document discusses the future of information management over the next 10 years. It notes that data is growing exponentially and will continue to do so. It identifies key requirements for data management like scalability, availability, security, and productivity. It then highlights how IBM's DB2 database on the System z mainframe platform meets these requirements through capabilities like industry-leading performance, security, workload consolidation, and reduced costs. The document concludes by discussing IBM's vision for harnessing big data through smart data analytics to help organizations make better, faster decisions.
This document discusses how hyperscale infrastructure approaches can enable enterprises to meet increasing future IT capacity needs with lower costs than traditional IT approaches. It describes how leading cloud providers have developed hyperscale computing models internally to dramatically improve efficiency and performance. The document proposes that operators and enterprises can adopt similar hyperscale infrastructure using disaggregated hardware architectures, which standardize components, abstract complexity, automate processes, and allow perpetual refresh of parts rather than entire systems. This would enable lower total cost of ownership through improvements like high utilization rates, reduced energy consumption, and eliminating forced hardware replacement cycles.
Ib Ms Vision For A Dynamic Infrastructuresimonarden
The document discusses IBM's vision for a Dynamic Infrastructure and what it means for CIOs. It outlines several issues driving the need for data center transformation, including rising costs and complexity that prevent quick innovation. It also explains how new technologies are driving exponential growth in data. IBM believes this calls for a recentralized approach to IT service delivery with more efficient computing that better aligns IT with business goals and allows organizations to take advantage of new opportunities.
TierPoint White Paper_When_Virtualization_Meets_Infrastructure_2015sllongo3
This white paper discusses how virtualization and cloud technologies are transforming businesses by reshaping IT infrastructure. It explores how virtualization allows businesses to improve flexibility, manageability, scalability and reduce costs when building cloud solutions. Key benefits include addressing challenges around rapidly growing data storage needs, cost savings through operational expenditures rather than capital expenditures, and improving disaster recovery capabilities through virtual infrastructure that can be restored in hours versus physical servers taking days.
Business unIntelligence - a Whistle Stop TourBarry Devlin
The old world of business intelligence is being transformed into a new biz-tech ecosystem. Analytics is forcing the recombination of operational and informational systems in a consistent and coherent IT environment for all business activities. Big data—despite the hype—introduces two very different types of information that transform how business processes interact with the external world. Together, these directions are driving a new BI, so different to its prior form that I call it “Business unIntelligence”. This session covers:
- Business drivers and results of the biz-tech ecosystem
- Modern conceptual and logical architectures for information, process and people
- Positioning of all forms of business analytic and big data
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Why Big Data Analytics Needs Business Intelligence Too Barry Devlin
Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.
In this April 2014 BrightTALK webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.
The document summarizes in-memory systems and how they enable faster and more informed decision making. It discusses how leading companies in various industries are exploring in-memory to improve decisions around staffing, dispatching, pricing and more. In-memory allows real-time processing of vast data volumes to gain insights where traditional systems took days or weeks. SAP has seen strong growth with its in-memory HANA platform. Innovation centers help users identify the right in-memory applications for their unique needs.
This document provides an overview of big data analytics, including:
1) It defines big data and describes its key characteristics of variety, velocity, and volume.
2) It outlines common types of big data like structured, unstructured, and semi-structured data.
3) It lists sources of big data such as social media, the cloud, the web, databases, and the Internet of Things.
4) It discusses challenges of big data like rapid growth, storage, security, and integrating diverse data sources.
The document discusses SimpliVity's OmniCube hyperconverged infrastructure platform. It aims to provide both cloud economics and enterprise capabilities like data protection, efficiency, performance and unified management. SimpliVity's key innovation is its data virtualization platform which performs real-time deduplication, compression and optimization of data without impacting performance. This allows data to be optimized once and accessed globally. The OmniCube combines compute, storage, networking and management into an integrated 2U building block to simplify IT infrastructure management and costs compared to traditional legacy stacks.
This document discusses business analytics and intelligence. It covers topics such as big data, structured vs unstructured data, databases, infrastructure, analytics evolution, and data visualization. Big data provides value when data sets are massive, though it can be expensive to store and process. Combining structured and unstructured data enables predictive analytics. NoSQL databases were developed to handle diverse data types at large scales. Cloud infrastructure provides benefits like streamlined IT management and widespread access to business intelligence across an organization. Analytics are evolving from internal data analysis to integrating diverse external data sources and building products using predictive insights. Data visualization is an important way to communicate findings from analytics, though the quality of the underlying data impacts the credibility of any visualizations.
This document discusses how colocation data centers provide an enabling infrastructure for enterprises and cloud providers to focus on their core missions rather than managing a distributed network of their own data centers. It argues that colocating infrastructure with expert data center operators provides significant advantages over maintaining proprietary data centers, including lower total costs, energy efficiency, scalability, security, and allowing consolidation of resources. By using tools to calculate the true costs of building and maintaining their own facilities versus colocating, companies can determine that colocation offers an economically superior solution to hosting their computing needs.
Big Data and Hadoop Training batch in Pune is scheduled to commence on December 7th, 2013.This batch will be as per a new revamped four day schedule, contents and focus, based on feedback from participants of earlier courses. The training is conducted in a workshop like environment with an effective blend of hands-on practicals and assignments to augment the fundamental theory covered.
About the Faculty:
He is a Doctorate in Engineering and an industry veteran with more than twenty five years experience in launching new technologies, products and businesses. He has been involved in acquiring five patents for the company that he has worked for.
Big Data Analytics – Why?
Data is now generated by more sources and at ever increasing rates. Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. The ability to process such data can provide that essential edge required for business success. Demand for Big Data professionals is rapidly increasing. Knowledge of Big Data can provide an advantage leading to faster professional advancement
About this course
This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together.
By completing this course you will be able to …
Understand fundamentals of analytics: Descriptive, Predictive and Prescriptive Analytics
Know what ‘Big Data’, Map Reduce and Hadoop are all about
Get a grip on the structure of Big Data applications
Effectively use Big Data techniques like Map Reduce and tools like Hadoop, Hive, Hbase, Pig
Choose the most appropriate tools to solve Big Data problems
Identify, propose and lead Big Data projects in your organizations
Course Content -
What is Big Data?
Overview of Big Data tools and techniques
In-depth coverage of Map-reduce techniques to manage Big Data
Hadoop - In Depth
HDFS – In Depth
Installing and managing Hadoop – Hands-on
Introduction to Hadoop Clusters
Hands-on session using native installation and Amazon EMR implementation of Hadoop
The Hadoop ecosystem: Pig, HIVE, HBase, Pig, SQOOP and Flume
Analytics: Descriptive, Predictive and Prescriptive
What is Big Data Analytics
Introducing Analytics in the enterprise: Case Studies
Trends in Big Data Analytics
The course takes a "hands-on" approach to ensure that the basics are understood very well and assimilated concepts are applied in practice.
Essential pre-requisite for practitioner course: Java programming language.
Note: Basic Java Module for participants those who are new to Java.
The document discusses the industry buzz around big data and the cloud. It provides an agenda for a webinar on these topics, including challenges of big data, architectural solutions using the cloud, and case studies. The document notes that data is growing exponentially and coming from more sources faster, creating challenges around complexity, validity, and linking diverse data sources. It argues the cloud can help address these challenges by providing vast, correlated, high confidence data to drive real-time predictions and recommendations.
This document discusses enabling analytics as a service (AaaS) on IBM SoftLayer Cloud. It describes how various analytical platforms and workloads have been modernized, migrated, and deployed on the SoftLayer Cloud to provide analytics capabilities as a service. Specifically, it outlines big data analytics platforms like Cloudera, Hortonworks, MapR, and IBM BigInsights that have been implemented on the cloud. It also discusses real-time analytics platforms like VoltDB and Apache Storm that have been deployed on SoftLayer Cloud to enable real-time analytics and processing of fast data streams.
This document compares information technology and information systems. It defines information technology as focusing on integrating computers and telecommunications for storing, retrieving, and managing data. Information systems are defined as coordinated networks that produce, distribute, and process information. The document outlines the objectives, layers, and differences between the two. Information technology focuses on technology standards, while information systems focus on identifying data needs and understanding technology's role in organizations. The document also discusses advantages and disadvantages of both information technology and information systems.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
The document discusses emerging trends in big data and analytics, including how expectations for business intelligence are changing with the growth of unstructured data sources. It covers challenges associated with integrating big data, and introduces concepts and tools like Hadoop, NoSQL databases, and textual ETL to address these challenges. The final sections discuss best practices for big data projects and provide examples of successful big data applications.
Ib Ms Vision For A Dynamic Infrastructuresimonarden
The document discusses IBM's vision for a Dynamic Infrastructure and what it means for CIOs. It outlines several issues driving the need for data center transformation, including rising costs and complexity that prevent quick innovation. It also explains how new technologies are driving exponential growth in data. IBM believes this calls for a recentralized approach to IT service delivery with more efficient computing that better aligns IT with business goals and allows organizations to take advantage of new opportunities.
TierPoint White Paper_When_Virtualization_Meets_Infrastructure_2015sllongo3
This white paper discusses how virtualization and cloud technologies are transforming businesses by reshaping IT infrastructure. It explores how virtualization allows businesses to improve flexibility, manageability, scalability and reduce costs when building cloud solutions. Key benefits include addressing challenges around rapidly growing data storage needs, cost savings through operational expenditures rather than capital expenditures, and improving disaster recovery capabilities through virtual infrastructure that can be restored in hours versus physical servers taking days.
Business unIntelligence - a Whistle Stop TourBarry Devlin
The old world of business intelligence is being transformed into a new biz-tech ecosystem. Analytics is forcing the recombination of operational and informational systems in a consistent and coherent IT environment for all business activities. Big data—despite the hype—introduces two very different types of information that transform how business processes interact with the external world. Together, these directions are driving a new BI, so different to its prior form that I call it “Business unIntelligence”. This session covers:
- Business drivers and results of the biz-tech ecosystem
- Modern conceptual and logical architectures for information, process and people
- Positioning of all forms of business analytic and big data
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Why Big Data Analytics Needs Business Intelligence Too Barry Devlin
Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.
In this April 2014 BrightTALK webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.
The document summarizes in-memory systems and how they enable faster and more informed decision making. It discusses how leading companies in various industries are exploring in-memory to improve decisions around staffing, dispatching, pricing and more. In-memory allows real-time processing of vast data volumes to gain insights where traditional systems took days or weeks. SAP has seen strong growth with its in-memory HANA platform. Innovation centers help users identify the right in-memory applications for their unique needs.
This document provides an overview of big data analytics, including:
1) It defines big data and describes its key characteristics of variety, velocity, and volume.
2) It outlines common types of big data like structured, unstructured, and semi-structured data.
3) It lists sources of big data such as social media, the cloud, the web, databases, and the Internet of Things.
4) It discusses challenges of big data like rapid growth, storage, security, and integrating diverse data sources.
The document discusses SimpliVity's OmniCube hyperconverged infrastructure platform. It aims to provide both cloud economics and enterprise capabilities like data protection, efficiency, performance and unified management. SimpliVity's key innovation is its data virtualization platform which performs real-time deduplication, compression and optimization of data without impacting performance. This allows data to be optimized once and accessed globally. The OmniCube combines compute, storage, networking and management into an integrated 2U building block to simplify IT infrastructure management and costs compared to traditional legacy stacks.
This document discusses business analytics and intelligence. It covers topics such as big data, structured vs unstructured data, databases, infrastructure, analytics evolution, and data visualization. Big data provides value when data sets are massive, though it can be expensive to store and process. Combining structured and unstructured data enables predictive analytics. NoSQL databases were developed to handle diverse data types at large scales. Cloud infrastructure provides benefits like streamlined IT management and widespread access to business intelligence across an organization. Analytics are evolving from internal data analysis to integrating diverse external data sources and building products using predictive insights. Data visualization is an important way to communicate findings from analytics, though the quality of the underlying data impacts the credibility of any visualizations.
This document discusses how colocation data centers provide an enabling infrastructure for enterprises and cloud providers to focus on their core missions rather than managing a distributed network of their own data centers. It argues that colocating infrastructure with expert data center operators provides significant advantages over maintaining proprietary data centers, including lower total costs, energy efficiency, scalability, security, and allowing consolidation of resources. By using tools to calculate the true costs of building and maintaining their own facilities versus colocating, companies can determine that colocation offers an economically superior solution to hosting their computing needs.
Big Data and Hadoop Training batch in Pune is scheduled to commence on December 7th, 2013.This batch will be as per a new revamped four day schedule, contents and focus, based on feedback from participants of earlier courses. The training is conducted in a workshop like environment with an effective blend of hands-on practicals and assignments to augment the fundamental theory covered.
About the Faculty:
He is a Doctorate in Engineering and an industry veteran with more than twenty five years experience in launching new technologies, products and businesses. He has been involved in acquiring five patents for the company that he has worked for.
Big Data Analytics – Why?
Data is now generated by more sources and at ever increasing rates. Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. The ability to process such data can provide that essential edge required for business success. Demand for Big Data professionals is rapidly increasing. Knowledge of Big Data can provide an advantage leading to faster professional advancement
About this course
This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together.
By completing this course you will be able to …
Understand fundamentals of analytics: Descriptive, Predictive and Prescriptive Analytics
Know what ‘Big Data’, Map Reduce and Hadoop are all about
Get a grip on the structure of Big Data applications
Effectively use Big Data techniques like Map Reduce and tools like Hadoop, Hive, Hbase, Pig
Choose the most appropriate tools to solve Big Data problems
Identify, propose and lead Big Data projects in your organizations
Course Content -
What is Big Data?
Overview of Big Data tools and techniques
In-depth coverage of Map-reduce techniques to manage Big Data
Hadoop - In Depth
HDFS – In Depth
Installing and managing Hadoop – Hands-on
Introduction to Hadoop Clusters
Hands-on session using native installation and Amazon EMR implementation of Hadoop
The Hadoop ecosystem: Pig, HIVE, HBase, Pig, SQOOP and Flume
Analytics: Descriptive, Predictive and Prescriptive
What is Big Data Analytics
Introducing Analytics in the enterprise: Case Studies
Trends in Big Data Analytics
The course takes a "hands-on" approach to ensure that the basics are understood very well and assimilated concepts are applied in practice.
Essential pre-requisite for practitioner course: Java programming language.
Note: Basic Java Module for participants those who are new to Java.
The document discusses the industry buzz around big data and the cloud. It provides an agenda for a webinar on these topics, including challenges of big data, architectural solutions using the cloud, and case studies. The document notes that data is growing exponentially and coming from more sources faster, creating challenges around complexity, validity, and linking diverse data sources. It argues the cloud can help address these challenges by providing vast, correlated, high confidence data to drive real-time predictions and recommendations.
This document discusses enabling analytics as a service (AaaS) on IBM SoftLayer Cloud. It describes how various analytical platforms and workloads have been modernized, migrated, and deployed on the SoftLayer Cloud to provide analytics capabilities as a service. Specifically, it outlines big data analytics platforms like Cloudera, Hortonworks, MapR, and IBM BigInsights that have been implemented on the cloud. It also discusses real-time analytics platforms like VoltDB and Apache Storm that have been deployed on SoftLayer Cloud to enable real-time analytics and processing of fast data streams.
This document compares information technology and information systems. It defines information technology as focusing on integrating computers and telecommunications for storing, retrieving, and managing data. Information systems are defined as coordinated networks that produce, distribute, and process information. The document outlines the objectives, layers, and differences between the two. Information technology focuses on technology standards, while information systems focus on identifying data needs and understanding technology's role in organizations. The document also discusses advantages and disadvantages of both information technology and information systems.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
The document discusses emerging trends in big data and analytics, including how expectations for business intelligence are changing with the growth of unstructured data sources. It covers challenges associated with integrating big data, and introduces concepts and tools like Hadoop, NoSQL databases, and textual ETL to address these challenges. The final sections discuss best practices for big data projects and provide examples of successful big data applications.
Este documento presenta dos ejercicios sobre probabilidad. El ejercicio 7 calcula las probabilidades de padecer hipertensión arterial, hiperlipemia o ambas entre pacientes. El ejercicio 8 analiza los días de estancia en un hospital, encontrando que siguen una distribución normal y calcula las probabilidades de estancias menores a 10 días y entre 8 y 13 días.
150609 benut de diversiteit aan contracten: hybride contracten en crow procon...CROW
Presentaties van de bijeenkomst CROW ProContract en hybride contracten 9 juni 2015.
Sprekers: Ad van Leest (CROW), Walter Suy (CROW), Bart Haring (Gemeente Hoorn), Niels Meijerink (CROW), Wendy Kooijman (Waterschap Groot Salland)
1. The document describes Romanesque and Gothic art and architecture in Europe during the 11th-13th centuries.
2. Monasteries were centers of prayer and culture during this time, and religious art was used to bring the faithful closer to God through symbolic imagery with didactic purposes.
3. Gothic architecture featured tall churches with pointed arches, flying buttresses, and stained glass windows that illuminated the interior with colorful light. Sculpture and paintings served religious and educational functions through biblical themes and were becoming more realistic and expressive.
In the Age of Unstructured Data, Enterprise-Class Unified Storage Gives IT a ...Hitachi Vantara
This document discusses the growing challenge of managing unstructured data in enterprises and proposes that unified storage is a solution. It outlines 3 trends driving greater adoption of file-based protocols and outlines 7 key elements that an ideal unified storage system for enterprises should have, including virtualization, intelligent tiering, flash optimization, and more. It then describes how Hitachi's VSP G1000 unified storage system meets all these elements to provide an enterprise-grade solution for unified storage without compromise.
IBM’s Storage Hypervisor concept is the best news since server-less back-up, and easier to understand. Thanks to the popularity of server virtualization, most business folks have some idea of what a hypervisor can do but a storage hypervisor has the potential to be much more transformative...
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
The document discusses choosing a secondary storage solution for G&J Consultation Sdn Bhd, which is facing performance bottlenecks impacting business productivity. It considers network attached storage (NAS) which offers benefits like easy setup, ease of use, scalability, and reliability. NAS allows for file sharing across a network and can improve performance by offloading tasks from email servers. The document also discusses factors to consider in secondary storage like performance, transfer speeds, scalability, and reliability.
Converged infrastructure bundles servers, storage, networking equipment, and management software into a single optimized system. This centralized approach improves efficiency by consolidating resources and increasing utilization rates. It helps address challenges of growing data volumes, limited resources, and management complexity that arise from independent "silos" of server, storage, and network infrastructure. Converged infrastructure provides cost savings and simplifies administration compared to disparate systems. Major technology companies compete in providing converged infrastructure solutions to organizations facing data growth and management challenges.
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.
This document discusses big data, including its definition, challenges, sources, types (volume, velocity, variety), and applications in various domains like engineering design, ecommerce, and product lifecycle management. It notes that big data is growing exponentially due to increased data collection and requires new technologies and architectures to process. The document outlines advantages of big data like improved innovation, customer satisfaction, and risk analysis.
The document discusses IBM's vision for smarter computing and smarter storage solutions. It can be summarized as:
1) IBM sees five mega-trends accelerating IT, including increased focus on security, cloud adoption, and unlocking insights from big data. Their smarter computing approach aims to make infrastructure cloud ready, data ready, and security ready.
2) IBM's smarter storage solutions help organizations manage big data growth, improve performance and productivity, and increase access to information. Their portfolio includes flash storage, data tiering technologies, and cloud and software solutions.
3) Adopting smarter computing and storage from IBM can provide benefits like reduced costs, improved efficiencies, higher data utilization, and enabling organizations to extract
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.
Here are some ways people who cannot read the alphabet can still learn to read:
- Learn to read braille. Braille uses patterns of raised dots that can be read with the fingers. It allows blind and visually impaired people to read text independently.
- Use assistive technology like screen readers. Screen readers are programs or devices that read digital text out loud. They work with many electronic devices like computers, smartphones, and e-readers. This allows non-alphabet readers to access written content.
- Memorize common symbols, logos, or pictograms. Many public signs, labels, and icons use universal visuals instead of words. With exposure and practice, non-readers can learn to interpret these symbols.
IRJET- A Novel Framework for Three Level Isolation in Cloud System based ...IRJET Journal
This document proposes a novel three-level isolation framework for cloud storage based on fog computing. The framework aims to address privacy and security issues in cloud storage by distributing user data across three layers - cloud servers, fog servers, and local machines. It uses a hash-Solomon encoding algorithm to split user data into multiple shares and store each share in a different layer. This provides three-way redundancy to protect against data loss and enhances security by isolating data across multiple environments. Theoretical analysis and experimental evaluation demonstrate the feasibility and security improvements of the proposed three-level isolation framework compared to existing cloud storage schemes.
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.
This document provides an overview of data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decisions. The document discusses why data warehousing differs from operational systems, sample data warehouse designs, and the mechanics of the design process including interviewing users, assembling teams, hardware/software choices, and handling aggregates.
Securing Your Future: Cloud-Based Data Protection SolutionsMaryJWilliams2
Explore the essential strategies for safeguarding your data with cloud-based protection solutions. This comprehensive guide delves into the benefits of using cloud services for data security, including enhanced scalability, reliability, and disaster recovery capabilities. Learn about the latest trends, best practices, and how to effectively implement cloud-based data protection to ensure your data is secure, accessible, and recoverable. To Know more: https://stonefly.com/white-papers/cloud-based-data-protection-strategies/
Securing the Future: A Guide to Cloud-Based Data ProtectionMaryJWilliams2
In an era where data breaches and cyber threats are increasingly common, cloud-based data protection emerges as a critical pillar for safeguarding digital assets. This article offers an in-depth exploration of cloud-based data protection strategies, tools, and best practices. Discover how leveraging the cloud can enhance your organization's data security posture, ensure business continuity, and provide scalability to meet future demands. To Know more: https://stonefly.com/white-papers/cloud-based-data-protection-strategies/
Read the Discussions below and give a good replyDiscussion 1..docxmakdul
Read the Discussions below and give a good reply
Discussion 1.
Information systems infrastructure consists the procedures of Software, Hardware, telecommunications, Networks managed by various specialists. Information systems are complementary networks like an organization that transcend information. Mainly it has 7 main components like Hardware platforms, Operating Systems, Software applications etc.
Information is data given meaning usually through some form of processing and combination with other data. Data is one of individual fact. An information system that collects, processes, manipulates, stores and communicates data according to a set of rules. It may include a methodology for update and feedback.
Usually, we can see information systems as two types. 1. Simple information systems 2. Complex Information systems.
A simple information system can be represented by Rolodex of names, addresses and telephone numbers
A complex information system could be a computer capable of storing the information on many Rolodexes, plus pictures, likes and dislikes, appointments and correspondence, organizing it for retrieval, a keyboard for input, a screen to view it, a printer for retrieval, a disk drive to store it and software to manage it.
Commonly an information system may only refer to a database management system which handles all the functions of collecting, managing, storing and retrieving the Rolodex information. Commonly today’s technological society, information systems are thought of within the context of the technology such as computers and software, but that need not be a case. As noted earlier, a Rolodex is also an information system
IS Evolution: Technology evaluation has impacted our lives positively over the last two decades so we should expect the same or similar outcomes from the future. If we observe the IT infrastructure evaluation, we can find several implementations from Enterprise computing to Cloud and mobile computing. Due to the implemental changes in Information systems, technology revolution happened over two decades.
Now an estimated 2.3 billion people worldwide using internet access and it became affordable. Technological advancements have had affects in all areas Health, Advertisement, Finance, Entertainment, just anything we can think about.
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Discussion 2.
In 1960s a 5 MB of capacity was acquired a truck and now we can see terabytes of information in our grasp. This is an advancement of information frameworks. Today a huge number of clients are making information regarding content, voice, video and so forth. The association of this information is a major test for a portion of the organizations. Presently we are talking not as far as Gigabytes or Terabytes but rather Zettabyte (1000000000 TB).
So as to deal with this information three noteworthy developing patterns are approaching:
1. Democratization of Data: By making the information fair implies that information ought to be accessible for all. There ...
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6. IBM Smarter Storage: Shat a Smarter Idea
Page 6
Analyst Name: David Hill
Topic Area: Storage
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Phone: (781) 326-0038
email the author: davidhill@mesabigroup.com
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Commentary
clouds. A compute cloud integrates
servers, networking, and storage
along with the applications. A
storage cloud’s sole purpose is to
provide storage. One variant is an
application centric storage cloud
where files and objects are adminis-
tered, secured, and self-provisioned
by an application, A standalone
storage cloud is one where the
consumer self-provisions his/her
requirements and is billed for
services rendered.
Illustrating Cloud Agile
Compute Cloud — Tight integration
with hypervisors such as VMware,
PowerVM, Hyper-V, or KVM delivers an
integrated server, storage, and network-
ing platform that can serve as the heart of
a compute cloud. Mid-range customers
can achieve their cloud goals by combin-
ing BladeCenter for Cloud Flash with
Storwize V7000 and Easy Tier, while
enterprises can achieve theirs with XIV
and Flash Cache.
Application-centric Storage Cloud
— IBM offers Scale Out Network At-
tached Storage (SONAS) and Storwize
V7000 Unified to serve as the foundation
for this type of storage cloud.
Stand-alone self-service storage
cloud portal — IBM has announced
intentions for SONAS and Storwize
V7000 Unified capability to be available
to support this type of storage cloud in
the fourth quarter of 2012 (and is now in
Beta).
Conclusions
Business as usual is not possible for
the information infrastructure,
including storage. On the storage
side, the continued explosion of data
accompanied by tight IT budgets
cannot be totally offset by the impact
of Moore’s Law on the server side and
similar effects on the storage side.
From a storage perspective therefore,
IBM’s Smarter Storage offers a
perspective on how to deal with this
situation that not only manages the
entire storage services infrastructure
more efficiently and productively, but
may even be able to free up some
scarce IT resources to devote to
much need innovative activities.
But IT has to take a deep breath and
view the storage infrastructure as a
whole rather than as a task specific
exercise (such as storage provision-
ing and backup recovery). That
requires putting together a storage-
services perspective of the infrastruc-
ture that is data centric and not
media centric as well as covering a
spectrum of services on not only a
single array, but also spanning the
entire storage infrastructure. At that
point IT can apply the three IBM
tenets for Smarter Storage — efficient
by design, self-optimizing, and cloud
agile — and start reaping the benefits
of the Smarter Storage approach.
David Hill