Company YahooGroup Research ProjectIntroductionThe .docx
•Download as DOCX, PDF•
0 likes•2 views
Company: Yahoo Group Research Project Introduction The growth and explosion of the data has led to land scale differences across several industries. Over the past two decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analyzing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amounts of stored data contain knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support. .
Report
Share
Report
Share
Recommended
Analysis on big data concepts and applications
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
An Encyclopedic Overview Of Big Data Analytics
This document provides an overview of big data analytics. It discusses the characteristics of big data, known as the 5 V's: volume, velocity, variety, veracity, and value. It describes how Hadoop has become the standard for storing and processing large datasets across clusters of servers. The challenges of big data are also summarized, such as dealing with the speed, scale, and inconsistencies of data from a variety of structured and unstructured sources.
Influence of Hadoop in Big Data Analysis and Its Aspects
This paper is an effort to present the basic understanding of BIG DATA and
HADOOP and its usefulness to an organization from the performance perspective. Along-with the
introduction of BIG DATA, the important parameters and attributes that make this emerging concept
attractive to organizations has been highlighted. The paper also evaluates the difference in the
challenges faced by a small organization as compared to a medium or large scale operation and
therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial
scale, open source programming system committed to adaptable, disseminated, information
concentrated processing. A number of application examples of implementation of BIG DATA across
industries varying in strategy, product and processes have been presented. This paper also deals
with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has
emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for
effectively composing requisitions which prepare boundless measures of information (multi-terabyte
information sets) in- parallel on extensive bunches of merchandise fittings in a dependable,
shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and
"reducer" which have been examined in this paper. The paper deals with the overall architecture of
HADOOP along with the details of its various components in Big Data.
R180305120123
This document discusses challenges and solutions related to big data implementation. Some key challenges mentioned include reluctance to invest in big data strategies, integrating traditional and big data, and finding professionals with both big data and domain skills. The document recommends starting small with proofs of concept and taking an iterative approach to derive early benefits from big data before making larger investments. It also stresses the importance of having an enterprise-wide data strategy and acquiring various skills needed for big data projects.
Ab cs of big data
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.
IRJET- A Comparative Study on Big Data Analytics Approaches and Tools
This document provides an overview of big data analytics approaches and tools. It begins with an abstract discussing the need to evaluate different methodologies and technologies based on organizational needs to identify the optimal solution. The document then reviews literature on big data analytics tools and techniques, and evaluates challenges faced by small vs large organizations. Several big data application examples across industries are presented. The document also introduces concepts of big data including the 3Vs (volume, velocity, variety), describes tools like Hadoop, Cloudera and Cassandra, and discusses scaling big data technologies based on an organization's requirements.
06. 9534 14985-1-ed b edit dhyan
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
Big Data and Big Data Management (BDM) with current Technologies –Review
The emerging phenomenon called ―Big Data‖ is pushing numerous changes in businesses and several other organizations, Domains, Fields, areas etc. Many of them are struggling just to manage the massive data sets. Big data management is about two things - ―Big data‖ and ―Data Management‖ and these terms work together to achieve business and technology goals as well. In previous few years data generation have tremendously enhanced due to digitization of data. Day by day new computer tools and technologies for transmission of data among several computers through Internet is been increasing. It‗s relevance and importance in the context of applicability, usefulness for decision making, performance improvement etc in all areas have emerged very fast to be relevant in today‗s era. Big data management also has numerous challenges and common complexities include low organizational maturity relative to big data, weak business support, and the need to learn new technology approaches. This paper will discuss the impacts of Big Data and issues related to data management using current technologies
Recommended
Analysis on big data concepts and applications
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
An Encyclopedic Overview Of Big Data Analytics
This document provides an overview of big data analytics. It discusses the characteristics of big data, known as the 5 V's: volume, velocity, variety, veracity, and value. It describes how Hadoop has become the standard for storing and processing large datasets across clusters of servers. The challenges of big data are also summarized, such as dealing with the speed, scale, and inconsistencies of data from a variety of structured and unstructured sources.
Influence of Hadoop in Big Data Analysis and Its Aspects
This paper is an effort to present the basic understanding of BIG DATA and
HADOOP and its usefulness to an organization from the performance perspective. Along-with the
introduction of BIG DATA, the important parameters and attributes that make this emerging concept
attractive to organizations has been highlighted. The paper also evaluates the difference in the
challenges faced by a small organization as compared to a medium or large scale operation and
therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial
scale, open source programming system committed to adaptable, disseminated, information
concentrated processing. A number of application examples of implementation of BIG DATA across
industries varying in strategy, product and processes have been presented. This paper also deals
with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has
emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for
effectively composing requisitions which prepare boundless measures of information (multi-terabyte
information sets) in- parallel on extensive bunches of merchandise fittings in a dependable,
shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and
"reducer" which have been examined in this paper. The paper deals with the overall architecture of
HADOOP along with the details of its various components in Big Data.
R180305120123
This document discusses challenges and solutions related to big data implementation. Some key challenges mentioned include reluctance to invest in big data strategies, integrating traditional and big data, and finding professionals with both big data and domain skills. The document recommends starting small with proofs of concept and taking an iterative approach to derive early benefits from big data before making larger investments. It also stresses the importance of having an enterprise-wide data strategy and acquiring various skills needed for big data projects.
Ab cs of big data
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.
IRJET- A Comparative Study on Big Data Analytics Approaches and Tools
This document provides an overview of big data analytics approaches and tools. It begins with an abstract discussing the need to evaluate different methodologies and technologies based on organizational needs to identify the optimal solution. The document then reviews literature on big data analytics tools and techniques, and evaluates challenges faced by small vs large organizations. Several big data application examples across industries are presented. The document also introduces concepts of big data including the 3Vs (volume, velocity, variety), describes tools like Hadoop, Cloudera and Cassandra, and discusses scaling big data technologies based on an organization's requirements.
06. 9534 14985-1-ed b edit dhyan
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
Big Data and Big Data Management (BDM) with current Technologies –Review
The emerging phenomenon called ―Big Data‖ is pushing numerous changes in businesses and several other organizations, Domains, Fields, areas etc. Many of them are struggling just to manage the massive data sets. Big data management is about two things - ―Big data‖ and ―Data Management‖ and these terms work together to achieve business and technology goals as well. In previous few years data generation have tremendously enhanced due to digitization of data. Day by day new computer tools and technologies for transmission of data among several computers through Internet is been increasing. It‗s relevance and importance in the context of applicability, usefulness for decision making, performance improvement etc in all areas have emerged very fast to be relevant in today‗s era. Big data management also has numerous challenges and common complexities include low organizational maturity relative to big data, weak business support, and the need to learn new technology approaches. This paper will discuss the impacts of Big Data and issues related to data management using current technologies
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...
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.
The ABCs of Big Data
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.
Big data Ppt
This document discusses big data, including its definition, characteristics of volume, velocity, and variety. It describes sources of big data like administrative data, transactions, public data, sensor data, and social media. It discusses processing big data using techniques like Hadoop MapReduce. It outlines benefits like real-time decision making but also drawbacks like security, privacy, and performance issues. It provides some facts about the size of data generated daily by companies and potential impacts and future growth of the big data industry and job market.
1
This document provides an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
Sample
The document discusses big data analytics, including its characteristics, tools, and applications. It defines big data analytics as the application of advanced analytics techniques to large datasets. Big data is characterized by its volume, variety, and velocity. New tools and methods are needed to store, manage, and analyze big data. The document reviews different big data storage, processing, and analytics tools and methods that can be applied in decision making.
Big data
BIG DATA ANALYTICS, Major players, When ,What & What of BIG Data,Hadoop and its components,HDFS & MapReduce
Big data
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
bcg-a-new-architecture-to-manage-data-costs-and-complexity-feb-2023.pdf
The document discusses three trends that are reshaping the data landscape and straining companies' data architectures: 1) exponentially growing data volumes and velocity, 2) increasingly accessible and specialized data use cases, and 3) shifting data economics due to cloud adoption. This is leading to issues like architectural complexity from vendor proliferation, fragmented and siloed data stacks, and rapidly doubling costs of data management. To effectively manage these challenges, the document argues that companies will need to adopt a fundamentally different, more federated and distributed data architecture approach going forward.
Big Data-Survey
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
IRJET - Big Data Analysis its Challenges
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
elgendy2014.pdf
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
Big Data Challenges faced by Organizations
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
BIG DATA & DATA ANALYTICS
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Big Data idea implementation in organizations: potential, roadblocks
This document discusses big data implementation in organizations and the potential opportunities and challenges. It begins by defining big data and explaining the key drivers of increasing data volumes, such as social media, the internet of things, and multimedia data. It then outlines some of the major benefits organizations can gain from big data, including improved decision making, customer experiences, and sales. However, successfully implementing big data also faces roadblocks such as selecting the right tools, techniques, and data sources. The document provides examples of technologies, methods, and skills needed to optimize big data initiatives.
Dealing with Big Data: Planning for and Surviving the Petabyte Age
When it comes to "big data," organizations need to focus on both technology and usability aspects to survive the Petabyte Age.
Gartner eBook on Big Data
Big Data Means Big Business
Big data has the potential to disrupt existing businesses and help create new ones by extracting useful information from huge volumes of structured and unstructured data. To realize this promise, organizations need cheap storage, faster processing, smarter software, and access to larger and more diverse data sets. Big data can unlock new business value by enabling better-informed decisions, discovering hidden insights, and automating business processes. While the technology is available, organizations must also invest in skills, cultural change, and using information as a corporate asset to fully leverage big data.
Convergence of AI, IoT, Big Data and Blockchain: A Review. Kefa Rabah .
Convergence of AI, IoT, Big Data and Blockchain: A Review.
Kefa Rabah .
Mara Research, Nairobi, Kenya .
Abstract
Data is the lifeblood of any business. Today, big data has applications in just about every industry – retail, healthcare,
financial services, government, agriculture, customer service among others. Any organization that can assimilate data
to answer nagging questions about their operations can benefit from big data. In overall, the demand for big data
transcend across all sectors and business. Those who work to understand their customers’ business and their problems
will be able to proactively identify big data solutions appropriate to their needs, and thus gain competitive advantage
over their competitors. Job demand for people with big data skill-set is also in the rise especially professional,
scientific and technical services; information technology; manufacturing; and finance and insurance; and retail.
DevOps is baseless without the cloud. IoT needs cloud to operate efficiently, for computing is required by the cloud
operate efficiently. AI remained only as model up until the advent of big data. Blockchain and related distributed
ledger technologies are disrupting the technology sector as we know it. The confluence of technologies is just
inevitable and often they are beneficial especially today when usher in the 4th industrial revolution (Rabah, 2017a)
and the forth coming machine economy (Rabah, 2018). More-so, data is a key ingredient of approaches to developing
AI and machine learning, which are now being applied to a wide variety of uses, from stock trading to chatbots to
self-driving cars. There is barely a business or human activity today that is not considered as a target for AI in future
years and decades.
Big Data at a Glance
Big data is the recent trend within different verticals of industries today. Read Whitepaper for enhanced big data solutions.
Bigdata
This document discusses big data analytics and how it is transforming business intelligence. It defines big data analytics as combining large datasets ("big data") with advanced analytic techniques. It describes big data using the three V's: volume, variety, and velocity. Volume refers to the large size of datasets. Variety means data comes from many different sources and formats. Velocity means data streams in continuously and in real-time. The document provides examples of how companies are using big data analytics to discover new insights and track changing customer behavior.
Hadoop and Big Data Readiness in Africa: A Case of Tanzania
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
Compare and contrast the Federalist and Democratic-Republican atti.docx
Compare and contrast the Federalist and Democratic-Republican attitudes toward the national government. Include a clear discussion of the differences their leaders held.
Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations.
.
Compare and contrast the Enlightenment (Rousseau, Kant, Emerson) wit.docx
Compare and contrast the Enlightenment (Rousseau, Kant, Emerson) with the Post-Enlightenment (Dostoyevsky, Nietzsche, Freud, Woolf, Achebe) theory of personhood. What, at base, is the human person in terms of its essential facilities and activities? Next, provide an argument that links your authors’ competing views of selfhood to their views about political association. That is, given what we are, how do they think we should govern and be governed? Finally, explain with argumentation what a ‘happy’ person would be in both eras, given the conditions of those forms of political association.
.
More Related Content
Similar to Company YahooGroup Research ProjectIntroductionThe .docx
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...
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.
The ABCs of Big Data
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.
Big data Ppt
This document discusses big data, including its definition, characteristics of volume, velocity, and variety. It describes sources of big data like administrative data, transactions, public data, sensor data, and social media. It discusses processing big data using techniques like Hadoop MapReduce. It outlines benefits like real-time decision making but also drawbacks like security, privacy, and performance issues. It provides some facts about the size of data generated daily by companies and potential impacts and future growth of the big data industry and job market.
1
This document provides an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
Sample
The document discusses big data analytics, including its characteristics, tools, and applications. It defines big data analytics as the application of advanced analytics techniques to large datasets. Big data is characterized by its volume, variety, and velocity. New tools and methods are needed to store, manage, and analyze big data. The document reviews different big data storage, processing, and analytics tools and methods that can be applied in decision making.
Big data
BIG DATA ANALYTICS, Major players, When ,What & What of BIG Data,Hadoop and its components,HDFS & MapReduce
Big data
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
bcg-a-new-architecture-to-manage-data-costs-and-complexity-feb-2023.pdf
The document discusses three trends that are reshaping the data landscape and straining companies' data architectures: 1) exponentially growing data volumes and velocity, 2) increasingly accessible and specialized data use cases, and 3) shifting data economics due to cloud adoption. This is leading to issues like architectural complexity from vendor proliferation, fragmented and siloed data stacks, and rapidly doubling costs of data management. To effectively manage these challenges, the document argues that companies will need to adopt a fundamentally different, more federated and distributed data architecture approach going forward.
Big Data-Survey
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
IRJET - Big Data Analysis its Challenges
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
elgendy2014.pdf
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
Big Data Challenges faced by Organizations
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
BIG DATA & DATA ANALYTICS
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Big Data idea implementation in organizations: potential, roadblocks
This document discusses big data implementation in organizations and the potential opportunities and challenges. It begins by defining big data and explaining the key drivers of increasing data volumes, such as social media, the internet of things, and multimedia data. It then outlines some of the major benefits organizations can gain from big data, including improved decision making, customer experiences, and sales. However, successfully implementing big data also faces roadblocks such as selecting the right tools, techniques, and data sources. The document provides examples of technologies, methods, and skills needed to optimize big data initiatives.
Dealing with Big Data: Planning for and Surviving the Petabyte Age
When it comes to "big data," organizations need to focus on both technology and usability aspects to survive the Petabyte Age.
Gartner eBook on Big Data
Big Data Means Big Business
Big data has the potential to disrupt existing businesses and help create new ones by extracting useful information from huge volumes of structured and unstructured data. To realize this promise, organizations need cheap storage, faster processing, smarter software, and access to larger and more diverse data sets. Big data can unlock new business value by enabling better-informed decisions, discovering hidden insights, and automating business processes. While the technology is available, organizations must also invest in skills, cultural change, and using information as a corporate asset to fully leverage big data.
Convergence of AI, IoT, Big Data and Blockchain: A Review. Kefa Rabah .
Convergence of AI, IoT, Big Data and Blockchain: A Review.
Kefa Rabah .
Mara Research, Nairobi, Kenya .
Abstract
Data is the lifeblood of any business. Today, big data has applications in just about every industry – retail, healthcare,
financial services, government, agriculture, customer service among others. Any organization that can assimilate data
to answer nagging questions about their operations can benefit from big data. In overall, the demand for big data
transcend across all sectors and business. Those who work to understand their customers’ business and their problems
will be able to proactively identify big data solutions appropriate to their needs, and thus gain competitive advantage
over their competitors. Job demand for people with big data skill-set is also in the rise especially professional,
scientific and technical services; information technology; manufacturing; and finance and insurance; and retail.
DevOps is baseless without the cloud. IoT needs cloud to operate efficiently, for computing is required by the cloud
operate efficiently. AI remained only as model up until the advent of big data. Blockchain and related distributed
ledger technologies are disrupting the technology sector as we know it. The confluence of technologies is just
inevitable and often they are beneficial especially today when usher in the 4th industrial revolution (Rabah, 2017a)
and the forth coming machine economy (Rabah, 2018). More-so, data is a key ingredient of approaches to developing
AI and machine learning, which are now being applied to a wide variety of uses, from stock trading to chatbots to
self-driving cars. There is barely a business or human activity today that is not considered as a target for AI in future
years and decades.
Big Data at a Glance
Big data is the recent trend within different verticals of industries today. Read Whitepaper for enhanced big data solutions.
Bigdata
This document discusses big data analytics and how it is transforming business intelligence. It defines big data analytics as combining large datasets ("big data") with advanced analytic techniques. It describes big data using the three V's: volume, variety, and velocity. Volume refers to the large size of datasets. Variety means data comes from many different sources and formats. Velocity means data streams in continuously and in real-time. The document provides examples of how companies are using big data analytics to discover new insights and track changing customer behavior.
Hadoop and Big Data Readiness in Africa: A Case of Tanzania
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
Similar to Company YahooGroup Research ProjectIntroductionThe .docx (20)
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...
bcg-a-new-architecture-to-manage-data-costs-and-complexity-feb-2023.pdf
bcg-a-new-architecture-to-manage-data-costs-and-complexity-feb-2023.pdf
Big Data idea implementation in organizations: potential, roadblocks
Big Data idea implementation in organizations: potential, roadblocks
Dealing with Big Data: Planning for and Surviving the Petabyte Age
Dealing with Big Data: Planning for and Surviving the Petabyte Age
Convergence of AI, IoT, Big Data and Blockchain: A Review. Kefa Rabah .
Convergence of AI, IoT, Big Data and Blockchain: A Review. Kefa Rabah .
Hadoop and Big Data Readiness in Africa: A Case of Tanzania
Hadoop and Big Data Readiness in Africa: A Case of Tanzania
More from janthony65
Compare and contrast the Federalist and Democratic-Republican atti.docx
Compare and contrast the Federalist and Democratic-Republican attitudes toward the national government. Include a clear discussion of the differences their leaders held.
Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations.
.
Compare and contrast the Enlightenment (Rousseau, Kant, Emerson) wit.docx
Compare and contrast the Enlightenment (Rousseau, Kant, Emerson) with the Post-Enlightenment (Dostoyevsky, Nietzsche, Freud, Woolf, Achebe) theory of personhood. What, at base, is the human person in terms of its essential facilities and activities? Next, provide an argument that links your authors’ competing views of selfhood to their views about political association. That is, given what we are, how do they think we should govern and be governed? Finally, explain with argumentation what a ‘happy’ person would be in both eras, given the conditions of those forms of political association.
.
Compare and contrast the eruption styles of the Mauna Loa Volcano in.docx
Compare and contrast the eruption styles of the Mauna Loa Volcano in Hawaii and Mount Pinatubo in Philippines.
Note: Be sure to include a review of the plate tectonic setting, as well as the role of magma composition and viscosity at each location on the morphology of the volcano and eruption style.
Must have "ORIGINAL WORK", REFERENCES, AND CITES USED PLEASE
.
Compare and contrast the difference and similarities between the OSI.docx
Compare and contrast the difference and similarities between the OSI Model and TCP/IP Model.
Also, consider:
The layers
How is data processed during transmission
Minimum 3 pages (not including the title page, abstract and reference)
APA format.
.
Compare and contrast the different points of view about the federal .docx
Compare and contrast the different points of view about the federal government's relationship with civil rights as seen in Daniel Inouye's memoir Journey to Washington and the opinion from the Supreme Court case Korematsu v. the United States, 1944. Papers will examine how these sources present different experiences oh Japanese-American during World War II and how they demonstrate the federal government's oftentimes ambiguous support for civil rights.
.
Compare and contrast the defenses of Infancy, Intoxication, Igno.docx
Compare and contrast the defenses of Infancy, Intoxication, Ignorance, and Mistake. Provide examples of each.
The paper must be 1-2 pages.
Use proper APA formatting and citations, including ‘in-text’ citations.
Reference at least 2 outside credible resources.
Work must be submitted by Sunday 11:59 pm EST
.
Compare and Contrast the difficulties today to the 1970s. Remember t.docx
Compare and Contrast the difficulties today to the 1970s. Remember to examine the similarities and differences. (mention examples).
Please follow the link below to get example from the article.
https://www.cnn.com/2015/06/11/opinions/bianculli-seventies-tv/index.html
.
compare and contrast the culture by selecting only one.docx
compare and contrast the
culture
by selecting only
one
group
(each group contains three specific countries that you are to research) from the following list:
Group 3:
India, Philippines, and South Africa
Scholarly sources are to be used for the final research paper (Blogs, magazine articles, and newspaper articles are not considered scholarly).
Use multiple double-spaced paragraphs with centered main headings and left aligned subheadings
Cite with
in text
citations every paragraph
Elaborate in your own words, content that you found of interest and that relates to educational systems and student life in selected group of three countries by comparing and contrasting.
Do not copy/paste content from the article(s) or use word-for-word direct quotes ("").
Each paragraph in the Final Research Paper will require
in text
citations.
The Final Research Paper will consist of a Title Page, an Introduction (approx. one half of content), a Discussion section (five to six pages of content), a Conclusion (approx. one half page of content), and a References list (a minimum of five scholarly sources used in your research paper in APA format, double-spacing, and hanging indent).
Do not copy/paste content from the article(s) or use word-for-word direct quotes ("").
Use APA style format for page headers, Title Page, Discussion section, and References list.
Please be sure to cite where you found the information used in your research paper.
Use at least five scholarly sources for the information that you use in your research paper. Sources are included as a list of "References".
Note: Wikipedia, magazine articles, newspaper articles, and blogs cannot be used as sources in the research paper.
Conduct a spelling and grammar check on the Final Research Paper content.
Writing assignments uploaded to the Assignment folder will be automatically submitted to Turnitin.com.
.
Compare and contrast the culture in the U.S. with Mexico. Discus h.docx
Compare and contrast the culture in the U.S. with Mexico. Discus how you would take into account such differences in your international strategy.
Your response should be at least 200 words in length. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations.
David, F. (2011). 1.
Strategic management: concepts & cases
(Custom Edition ed., pp. 335-338). New York: McGraw-Hill Irwin.
No Wiki, Dictionary.com or Plagiarism
.
Compare and contrast the comments of Professor Kuhlman and Professor.docx
The professors see both similarities and differences in their analyses of women's suffrage movements. While they both identify factors that led to the enactment of voting rights, they provide different specific examples. Additionally, they raise concerns beyond just the right to vote, such as economic opportunities and protections, that suffrage alone did not fully address. There are arguments that women's suffrage in the U.S. was either an evolutionary, natural progression or a revolutionary, radical change, depending on how you view the social and political context at the time.
Compare and contrast the comments of Professor Kuhlman and Profess.docx
Compare and contrast the comments of Professor Kuhlman and Professor Woodworth-Ney. Do you find any similarities and/or any differences? Do the professors see any patterns or trends in the suffrage movements? What specific examples do they provide as factors in the enactment of women’s voting rights?
Besides the right to vote, what other concerns are presented by Professors Kuhlman, Woodworth-Ney and Foner? Were these concerns answered with suffrage? Why, or why not?
Given the professors' comments and Foner's text, do you think granting women the right to vote was evolutionary (a natural progression) or revolutionary (a radical change)? Why?
----------------------------
Podcast
Questioning Freedom: What was the significance of the Seneca Falls convention of 1848?
http://media.itrc.isu.edu/videos/?v=events/hist118/Give_Me_Liberty_Q69.flv&q=.dv
Questioning Freedom: How were women's lives changing in the late nineteenth century, and which direction was the women's movement taking?
http://media.itrc.isu.edu/videos/?v=events/hist118/Give_Me_Liberty_Q94.flv&q=.dv
*+*+*+*+*+*+*
Dr. Kuhlman's Questions
Was there any pattern to the granting of women's suffrage?
Suffrage Map
How did voting rights relate to nationhood?
Women and "nationhood"
Women's suffrage brought new rights. Were there still shortcomings after suffrage? Any items not gained with voting rights?
Gains for women
Shortcomings of women's suffrage
Did international events contribute to women's suffrage?
Suffrage Map
Dr. Woodworth-Ney's Questions
Do you find any pattern to women's suffrage in the American West?
U S Suffrage Map
Did suffrage proponents argue women voting would protect the status quo? What arguments were made in the American West in which womens' suffrage would maintain the status quo?
Texas Example
Were there any other arguments presented?
Other women's issues
Wyoming Example
.
Compare and contrast the Church Shooting that occurred on June 1.docx
Compare and contrast the Church Shooting that occurred on June 17, 2015, in Charleston, South Carolina to the shooting that occurred on December 29, 2019, in White Settlement, TX.
You must include:
The right-to-carry and/or concealed firearm laws in both locations, and how restrictions or allowances may have affected each outcome.
Your assignment is to be typed, single spaced, Verdana 12 font, and should be 4-5 pages in length. Margins’ top, bottom, left, and right should be set at one (1) inch.
NOTE: I won’t accept your responses without multiple citations from your book, peer review resources and/or citations from periodicals, valid news agencies, and any other valid publications. Grading for these assignments will be based on the Written Communication Rubric (see Rubric for grading points).
Your presentation is subject to review for text comparison by Turnitin, a plagiarism detection service (http://www.turnitin.com). Plagiarism will result in an automatic failure of the course, so if you quote something, give credit to the author. This assignment represents not only what you know, but the effort you put forth in understanding and presenting the material to others.
NOTE: Want a good grade, refer to the Rubric (posted on line) for grade preparation. Want a great grade, give me your best, and put your heart into it…LEARN! LEARN for yourself, and your friends and family! LEARN for your future!
Written Communication Rubric for
Level of Achievement
Evaluators assign a one (1) to any measure that does not meet benchmark level performance
Benchmark
2
Milestones
3 4
Capstone
5
Content and Development
Very limited development of content; (focus, relevance, purpose, explanations); little use/application of course concepts and terms to show knowledge of subject matter.
Adequate development of content; (focus, relevance, purpose, explanations); some use of course concepts and terms to show knowledge of subject matter.
Competent development of content (focus, relevance, purpose, explanations); appropriate use/application of course concepts & terms to show knowledge of subject matter.
Masterful development of content (focus, relevance, purpose, explanations); shows comprehensive knowledge of subject matter through full use/application of course concepts and terms.
Organization
Paragraphs had little to no organization.
Transitions were poor. Sequence of ideas is limited.
Some paragraphs were organized around topic sentences with some development. Some transitions were awkward. Sequence of ideas is adequate.
Most paragraphs were organized around topic sentences and were well developed. Most transitions were adequate. Sequence of ideas is competent.
All paragraphs were organized around topic sentences and fully developed. All transitions were used effectively. Sequence of ideas is effective.
Control of Syntax and Mechanics
Language, word choice, and sentence variety
Uses .
Compare and contrast the case studies in an effort to describe h.docx
Compare and contrast the case studies in an effort to describe highly effective organizations.
Then, assess the behavior and structure of the organizations. Explain what is effective, what is not effective, and identify specific approaches to make organizations more effective.
Use at least two credible sources
This assignment should be 1300 to 1500 words (4–5 pages) when completed.
CASE STUDY 1
At approximately 15:45 PM, Strike Team 2642 carefully navigated the one-lane gravel road that led to Rodgers Ridge, where they had been ordered to engage in some brush clearing and structural defense operations around three large vacation homes that were distributed across the ridge top. Captain Doug Chandler was leading a strike team for the first time, and he cautiously watched the two separate smoke columns that spouted from the 1600-acre Camp Creek Fire and the 2300-acre Jones Ranch Fire in the valley just beyond the ridge. For the moment, the wind was at their backs and would be pushing the fire away from the ridge.
The five units that made up Captain Chandler’s strike team were all heavy brush (HB) units, each staffed with one officer and three fire fighters and carrying 500 gallons (1892.5 liters) of water, foam, and the normal complement of tools. As they reached the large turnaround near the ridge top, the captain noted that all three homes were built within 50 ft (15.2 m) of each other, with one being the closest to the lip of the canyon, and the other two set back along a single driveway that led off the main road. At 4:20 PM, Captain Chandler ordered two of his brush units, HB26 and HB27, to proceed all the way down the driveway to the house at the end and to “prepare the residence house for defensive operations.” He sent brush units HB28 and HB29 to the two remaining houses and ordered them to prep those structures, while he stayed in a “lookout” position in HB25 near the driveway entrance.
At 4:27 PM, Captain Chandler received a call on his cell phone from the Operations Division. They informed him that the weather was due to change, and that he could expect temperatures to increase for another 30 minutes to a high of 94°F. Humidity was expected to stay extremely low, and the winds were to turn 180° by 7:00 PM. Captain Chandler in turn contacted his strike team by radio, telling them, “Strike Team 2642, all units, just be advised we’re due for a wind change.” He issued no further orders, and radio records showed that only HB26 acknowledged the message. Eighteen minutes later, at 4:45 PM, Captain Chandler in HB25 again communicated a message to his strike team: “ST 2642, all units, from my position it appears the wind has changed and may be pushing the fire toward the ridge.” This time, both HB26 and HB27 acknowledged, both with the single word “Copy.”
Just 12 minutes later, at 4:57 PM, HB26 frantically radioed that they were in danger. “HB26 and 27 here, we’ve … we’ve got lots of fire rolling toward us and it appears it’ll cut be.
Compare and contrast the artistic achievements of the Hellenistic pe.docx
Compare and contrast the artistic achievements of the Hellenistic period with those of the classical age. How were they different?
Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations.
Levack, B., Muir, E.,& Veldman, M. (2011).
The west encounters & transformations
. (3rd ed., Vol. 1, pp. 124-126, 128-130). Upper Saddle: Pearson. DOI: www.pearsonhighered.com
(Levack, Muir & Veldman, 2011)
No wiki, dictionary.com or plagarism
.
Compare and contrast the art works Sleeping Gypsy by Henri Roussea.docx
Compare and contrast the art works Sleeping Gypsy by Henri Rousseau and The Kiss by Gustav Klimt. Compare the characteristics of impressionism that are evident in both works of art and elements of shape, line and color that the artist use to create their artwork.
Write a question from the instructor’s perspective concerning impressionism and post-impressionism and how principles and elements played a part in that art movement.
Please completely address the topics
MLA format
.
Compare and contrast the arguments of Condon, Hynek, and Paynter reg.docx
Compare and contrast the arguments of Condon, Hynek, and Paynter regarding the existence of UFOs. Evaluate the conclusion that each person draws and the evidence that each uses to support his conclusion. Which person do you think makes the best argument? Why? Make certain that your post uses APA style and is grammatically correct.
Only 250 words please cite with in post
.
Compare and contrast the advantages and disadvantages of quantitativ.docx
Compare and contrast the advantages and disadvantages of quantitative and qualitative research. Discuss areas of disagreement.
REPLY TO MY CLASSMATE’S RESPONSE TO THE ABOVE QUESTION AND EXPLAIN WHY YOU AGREE? (A MINIMUM OF 150 WORDS)
Quantitative research is more scientifically done, and it is more of a controlled process. This form has been around for a long time and is typically the more trusted form of research. These are specific and standardized ways to perform survey’s, data collection, tests, interviews. This form of research involves usually only two variables. The researcher does their best to not influence the process as this will contaminate the findings. This form is used to find a relationship or correlation between 2 variables meaning, “how does more variable cause another to change.” When interviewing, surveying, or having participants take tests this process involves using closed ended questions, multiple choice and using the Likert way setting up these inquiries. The researcher in quantitative research must find many previous studies related to the subject and uses sources professional peer reviewed articles and journals. This form uses scientific method and formulas to find correlation.
Qualitative: In general, I feel that this form of research is (for myself) easier to understand. Meaning that this form uses a “real life” approach to it. It is typically in the past not known for its validity. Researchers in qualitative are very much a part of the whole process. They use self-reflection, personal experiences, and in general the role the researcher affects a large part of the design, participants, and the outcomes. Using a deductive method for understanding data, they go back and forth between findings to try and put together correlations. Researchers often in qualitative method study their own places of work or social environment by sitting and observing while taking notes. Collecting data for this type of research can involve sources like newspaper, diaries, social media, emails, and other subjective references. Participants in qualitative research are interactive with the researcher by interviewing participants with open questions to find opinions. This form of research has recently emerged in last decades. The researcher may need to have more understanding of study variables because there are not many sources of previous studies.
I think these following paragraphs from the article in the study guide help make it clearer the differences between quantitative and qualitative.
“The quantitative research methods derived from the natural sciences that emphasize objectivity, measurement, reliability and validity, have come to be increasingly inadequate especially in cross-cultural research. Attention has been devoted to a search for effective alternatives, and this leads to the revitalization of the qualitative approach which emphasizes the description of culture and meaning.”
“The distinction between quantitative and qua.
Compare and contrast the Ancient Greek and Ancient Roman conceptions.docx
Compare and contrast the Ancient Greek and Ancient Roman conceptions of the state. How do Plato and Cicero define key terms, such as citizenship, virtue, and state? And how do these affect their notions of an ideal form of government?
min 500 words with 1/2 references
.
Compare and contrast race and ethnicity, offering one to two points .docx
Compare and contrast race and ethnicity, offering one to two points of similarity (comparison) and one to two points of difference (contrast). What is symbolic ethnicity, and how does it relate to privilege? How do the differences between race and ethnicity underscore the privileged position of whites in America?
.
Compare and contrast primary and secondary research. Give examples.docx
Compare and contrast primary and secondary research. Give examples and explanations of each in your response.
No wiki, dictionary.com & cite all work
Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations.
Silva, K., & Howard, D. (2006). The Hospitality & Tourism Business.
Hospitality & Tourism
(pp. 192-195): McGraw-Hill.
.
More from janthony65 (20)
Compare and contrast the Federalist and Democratic-Republican atti.docx
Compare and contrast the Federalist and Democratic-Republican atti.docx
Compare and contrast the Enlightenment (Rousseau, Kant, Emerson) wit.docx
Compare and contrast the Enlightenment (Rousseau, Kant, Emerson) wit.docx
Compare and contrast the eruption styles of the Mauna Loa Volcano in.docx
Compare and contrast the eruption styles of the Mauna Loa Volcano in.docx
Compare and contrast the difference and similarities between the OSI.docx
Compare and contrast the difference and similarities between the OSI.docx
Compare and contrast the different points of view about the federal .docx
Compare and contrast the different points of view about the federal .docx
Compare and contrast the defenses of Infancy, Intoxication, Igno.docx
Compare and contrast the defenses of Infancy, Intoxication, Igno.docx
Compare and Contrast the difficulties today to the 1970s. Remember t.docx
Compare and Contrast the difficulties today to the 1970s. Remember t.docx
compare and contrast the culture by selecting only one.docx
compare and contrast the culture by selecting only one.docx
Compare and contrast the culture in the U.S. with Mexico. Discus h.docx
Compare and contrast the culture in the U.S. with Mexico. Discus h.docx
Compare and contrast the comments of Professor Kuhlman and Professor.docx
Compare and contrast the comments of Professor Kuhlman and Professor.docx
Compare and contrast the comments of Professor Kuhlman and Profess.docx
Compare and contrast the comments of Professor Kuhlman and Profess.docx
Compare and contrast the Church Shooting that occurred on June 1.docx
Compare and contrast the Church Shooting that occurred on June 1.docx
Compare and contrast the case studies in an effort to describe h.docx
Compare and contrast the case studies in an effort to describe h.docx
Compare and contrast the artistic achievements of the Hellenistic pe.docx
Compare and contrast the artistic achievements of the Hellenistic pe.docx
Compare and contrast the art works Sleeping Gypsy by Henri Roussea.docx
Compare and contrast the art works Sleeping Gypsy by Henri Roussea.docx
Compare and contrast the arguments of Condon, Hynek, and Paynter reg.docx
Compare and contrast the arguments of Condon, Hynek, and Paynter reg.docx
Compare and contrast the advantages and disadvantages of quantitativ.docx
Compare and contrast the advantages and disadvantages of quantitativ.docx
Compare and contrast the Ancient Greek and Ancient Roman conceptions.docx
Compare and contrast the Ancient Greek and Ancient Roman conceptions.docx
Compare and contrast race and ethnicity, offering one to two points .docx
Compare and contrast race and ethnicity, offering one to two points .docx
Compare and contrast primary and secondary research. Give examples.docx
Compare and contrast primary and secondary research. Give examples.docx
Recently uploaded
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【网址🎉ac44.net🎉】
【网址🎉ac44.net🎉】欧洲杯下注在体育博彩方面，不难发现，欧洲杯下注多为英式运动提供投注。欧洲杯下注为丰富多彩的体育项目提供投注，如足球、赛马、板球和飞镖，但这不是欧洲杯下注的唯一优势。在欧洲杯下注，全世界玩家都可以找到自己感兴趣的比赛进行投注。欧洲杯下注为网球和美式运动如棒球、美式足球、篮球、冰球开出的投注同样值得关注。在夏季或冬季奥运会方面，欧洲杯下注为玩家开出了丰富多彩的服务，是你的不二之选。你还可以投注水球、自行车和班迪球等小众体育项目，甚至可以为武术、拳击和综合格斗投注。欧洲杯下注为众多体育项目开出滚球投注。
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
How to Setup Default Value for a Field in Odoo 17
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Creation or Update of a Mandatory Field is Not Set in Odoo 17
In this slide we will discuss Creation or Updation of a mandatory field is not set in Odoo 17.
KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH
INDIA`S OWN LITERARY GENIUS MR.KHUSHWANT SINGH WAS TRULY A VERY BRAVE SOUL AND WAS AWARDED WITH THE MAGIC OF WORDS BY GOD.
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
How to Fix [Errno 98] address already in use
This slide will represent the cause of the error “[Errno 98] address already in use” and the troubleshooting steps to resolve this error in Odoo.
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER JEE PYQ
NIPER JEE QUESTIONS
MOST FREQUENTLY ASK QUESTIONS
NIPER MEMORY BASED QUWSTIONS
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Recently uploaded (20)
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
220711130088 Sumi Basak Virtual University EPC 3.pptx
220711130088 Sumi Basak Virtual University EPC 3.pptx
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Creation or Update of a Mandatory Field is Not Set in Odoo 17
Creation or Update of a Mandatory Field is Not Set in Odoo 17
KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH
KHUSWANT SINGH.pptx ALL YOU NEED TO KNOW ABOUT KHUSHWANT SINGH
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
Company YahooGroup Research ProjectIntroductionThe .docx
- 1. Company: Yahoo Group Research Project Introduction The growth and explosion of the data has led to land scale differences across several industries. Over the past two decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analyzing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amounts of stored data contain knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support.