Big data refers to terabytes or larger datasets that are generated daily and stored across multiple machines in different formats. Analyzing this data is challenging due to its size, format diversity, and distributed storage. Moving the data or code during analysis can overload networks. MapReduce addresses this by bringing the code to the data instead of moving the data, significantly reducing network traffic. It uses HDFS for scalable and fault-tolerant storage across clusters.
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Geoffrey Fox
Motivating Introduction to MOOC on Big Data from an applications point of view https://bigdatacoursespring2014.appspot.com/course
Course says:
Geoffrey motivates the study of X-informatics by describing data science and clouds. He starts with striking examples of the data deluge with examples from research, business and the consumer. The growing number of jobs in data science is highlighted. He describes industry trend in both clouds and big data.
He introduces the cloud computing model developed at amazing speed by industry. The 4 paradigms of scientific research are described with growing importance of data oriented version. He covers 3 major X-informatics areas: Physics, e-Commerce and Web Search followed by a broad discussion of cloud applications. Parallel computing in general and particular features of MapReduce are described. He comments on a data science education and the benefits of using MOOC's.
The document discusses how big data is used in ecommerce. It describes the four V's of big data (volume, velocity, variety, and veracity) and the value chain of big data including data origins, integration, analytics, and applications. Data sources include internal transactional data and external sources like Google Analytics. Analytics are used to measure marketing activities and key metrics like customer acquisition cost and lifetime value. Customer segmentation, product associations, personalization, dynamic pricing, and delivering customized content are also discussed.
Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and moreAmazon Web Services
This document discusses how companies can use Amazon Web Services (AWS) big data and analytics services like Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon DynamoDB, and Amazon Kinesis to gain insights from massive amounts of data. It provides examples of how companies in various industries like mobile, e-commerce, media, and gaming use these AWS services for use cases like recommendations, targeted advertising, fraud detection, and real-time analytics. The document also compares different AWS analytics services and discusses best practices for deploying big data solutions on AWS.
BIG Data & Hadoop Applications in E-CommerceSkillspeed
Explore the applications of BIG Data & Hadoop in eCommerce via Skillspeed.
BIG Data & Hadoop in eCommerce is a key differentiator, especially in terms of generating optimized customer & back-end experiences. They are used for tracking consumer behavior, optimizing logistics networks and forecasting demand - inventory cycles.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Big data has been a buzzword in today’s business world for the last couple of years and especially in the context of e-commerce. The textbook definition of big data is ‘data sets that are so large or complex that traditional data processing applications are inadequate to deal with them‘. When a person reads this definition, he has the right to assume that processing big data requires state-of-the-art computing technologies coupled with best-in-class engineering approaches and that is pretty much true. Today’s banks, large retailers, insurance companies and telecom giants are all searching for new methods and innovative vendors to be able to derive meaningful results from the vast sources of data they have on hand. If we specifically focus on the use of big data in ecommerce, we may comment that big data on the e-commerce websites and of course on social media never sleeps.
Looking for interesting content on e-commerce and personalization? Check this out : http://www.perzonalization.com/e-commerce-personalization-resources/
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
Big data refers to terabytes or larger datasets that are generated daily and stored across multiple machines in different formats. Analyzing this data is challenging due to its size, format diversity, and distributed storage. Moving the data or code during analysis can overload networks. MapReduce addresses this by bringing the code to the data instead of moving the data, significantly reducing network traffic. It uses HDFS for scalable and fault-tolerant storage across clusters.
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Geoffrey Fox
Motivating Introduction to MOOC on Big Data from an applications point of view https://bigdatacoursespring2014.appspot.com/course
Course says:
Geoffrey motivates the study of X-informatics by describing data science and clouds. He starts with striking examples of the data deluge with examples from research, business and the consumer. The growing number of jobs in data science is highlighted. He describes industry trend in both clouds and big data.
He introduces the cloud computing model developed at amazing speed by industry. The 4 paradigms of scientific research are described with growing importance of data oriented version. He covers 3 major X-informatics areas: Physics, e-Commerce and Web Search followed by a broad discussion of cloud applications. Parallel computing in general and particular features of MapReduce are described. He comments on a data science education and the benefits of using MOOC's.
The document discusses how big data is used in ecommerce. It describes the four V's of big data (volume, velocity, variety, and veracity) and the value chain of big data including data origins, integration, analytics, and applications. Data sources include internal transactional data and external sources like Google Analytics. Analytics are used to measure marketing activities and key metrics like customer acquisition cost and lifetime value. Customer segmentation, product associations, personalization, dynamic pricing, and delivering customized content are also discussed.
Big Data & Analytics - Use Cases in Mobile, E-commerce, Media and moreAmazon Web Services
This document discusses how companies can use Amazon Web Services (AWS) big data and analytics services like Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon DynamoDB, and Amazon Kinesis to gain insights from massive amounts of data. It provides examples of how companies in various industries like mobile, e-commerce, media, and gaming use these AWS services for use cases like recommendations, targeted advertising, fraud detection, and real-time analytics. The document also compares different AWS analytics services and discusses best practices for deploying big data solutions on AWS.
BIG Data & Hadoop Applications in E-CommerceSkillspeed
Explore the applications of BIG Data & Hadoop in eCommerce via Skillspeed.
BIG Data & Hadoop in eCommerce is a key differentiator, especially in terms of generating optimized customer & back-end experiences. They are used for tracking consumer behavior, optimizing logistics networks and forecasting demand - inventory cycles.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Big data has been a buzzword in today’s business world for the last couple of years and especially in the context of e-commerce. The textbook definition of big data is ‘data sets that are so large or complex that traditional data processing applications are inadequate to deal with them‘. When a person reads this definition, he has the right to assume that processing big data requires state-of-the-art computing technologies coupled with best-in-class engineering approaches and that is pretty much true. Today’s banks, large retailers, insurance companies and telecom giants are all searching for new methods and innovative vendors to be able to derive meaningful results from the vast sources of data they have on hand. If we specifically focus on the use of big data in ecommerce, we may comment that big data on the e-commerce websites and of course on social media never sleeps.
Looking for interesting content on e-commerce and personalization? Check this out : http://www.perzonalization.com/e-commerce-personalization-resources/
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
How to use the power of data in e-Commerce? Applying the Big Data solutions makes it possible to analyse data in real time. This allows us to use the data not for reports only, but to translate them into action.
The document acknowledges and thanks several people who helped with the completion of a seminar report. It expresses gratitude to the seminar guide for being supportive and compassionate during the preparation of the report. It also thanks friends who contributed to the preparation and refinement of the seminar. Finally, it acknowledges profound gratitude to the Almighty for making the completion of the report possible with their blessings.
Magento scalability from the trenches (Meet Magento Sweden 2016)Divante
This document discusses strategies for scaling a Magento e-commerce platform. It recommends first using vertical scaling by optimizing code and enabling caching before adding additional application and database servers through horizontal scaling. Specific optimizations discussed include using Redis for caching, Varnish for page caching, separating the database to its own server, enabling flat catalog indexing, and implementing master-slave database replication. Proper monitoring tools like New Relic and load testing are also emphasized for identifying bottlenecks during the scaling process.
Omnichannel Customer Experience. Companies such as Amazon, Facebook, Google, Apple already know that the future of user experience is automated interface creation depending on customer needs.
Surprising failure factors when implementing eCommerce and Omnichannel eBusinessDivante
We work on the large Omnichannel and eCommerce projects in Europe. Therefore, we can see from the inside how many companies approach this topic. Comparing it with the obtained results, we can determine positive and negative factors influencing success with great certainty. In this presentation we share stories of companies that are not mentioned in our case studies. These are the stories of bad choices, leading to failure.
Big data refers to the massive amounts of unstructured data that are growing exponentially. Hadoop is an open-source framework that allows processing and storing large data sets across clusters of commodity hardware. It provides reliability and scalability through its distributed file system HDFS and MapReduce programming model. The Hadoop ecosystem includes components like Hive, Pig, HBase, Flume, Oozie, and Mahout that provide SQL-like queries, data flows, NoSQL capabilities, data ingestion, workflows, and machine learning. Microsoft integrates Hadoop with its BI and analytics tools to enable insights from diverse data sources.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Satya Nadella is the Chief Executive Officer of Microsoft. Ayman Mohamed Mansour has successfully completed the requirements to become a Microsoft Specialist in Configuring Windows 7. He achieved this certification on December 01, 2015 with a certification number of F492-4448.
74 409 server virtualization with windows server hyper-v and system centerayman diab
Satya Nadella, CEO of Microsoft, certified AYMAN MOHAMED MANSOUR as a Microsoft Specialist in Server Virtualization with Windows Server Hyper-V and System Center on March 13, 2014. MANSOUR completed the requirements and was issued certification number E750-7285 to be recognized as a Microsoft Specialist in this area.
Microsoft Certified System Administrator (MCSA)ayman diab
Satya Nadella is the Chief Executive Officer of Microsoft. Ayman Mohamed Mansour has successfully completed the requirements to be recognized as a Microsoft Certified Solutions Associate: Windows Server 2008. He achieved this certification on April 16, 2012 with certification number D719-8215.
How to use the power of data in e-Commerce? Applying the Big Data solutions makes it possible to analyse data in real time. This allows us to use the data not for reports only, but to translate them into action.
The document acknowledges and thanks several people who helped with the completion of a seminar report. It expresses gratitude to the seminar guide for being supportive and compassionate during the preparation of the report. It also thanks friends who contributed to the preparation and refinement of the seminar. Finally, it acknowledges profound gratitude to the Almighty for making the completion of the report possible with their blessings.
Magento scalability from the trenches (Meet Magento Sweden 2016)Divante
This document discusses strategies for scaling a Magento e-commerce platform. It recommends first using vertical scaling by optimizing code and enabling caching before adding additional application and database servers through horizontal scaling. Specific optimizations discussed include using Redis for caching, Varnish for page caching, separating the database to its own server, enabling flat catalog indexing, and implementing master-slave database replication. Proper monitoring tools like New Relic and load testing are also emphasized for identifying bottlenecks during the scaling process.
Omnichannel Customer Experience. Companies such as Amazon, Facebook, Google, Apple already know that the future of user experience is automated interface creation depending on customer needs.
Surprising failure factors when implementing eCommerce and Omnichannel eBusinessDivante
We work on the large Omnichannel and eCommerce projects in Europe. Therefore, we can see from the inside how many companies approach this topic. Comparing it with the obtained results, we can determine positive and negative factors influencing success with great certainty. In this presentation we share stories of companies that are not mentioned in our case studies. These are the stories of bad choices, leading to failure.
Big data refers to the massive amounts of unstructured data that are growing exponentially. Hadoop is an open-source framework that allows processing and storing large data sets across clusters of commodity hardware. It provides reliability and scalability through its distributed file system HDFS and MapReduce programming model. The Hadoop ecosystem includes components like Hive, Pig, HBase, Flume, Oozie, and Mahout that provide SQL-like queries, data flows, NoSQL capabilities, data ingestion, workflows, and machine learning. Microsoft integrates Hadoop with its BI and analytics tools to enable insights from diverse data sources.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Satya Nadella is the Chief Executive Officer of Microsoft. Ayman Mohamed Mansour has successfully completed the requirements to become a Microsoft Specialist in Configuring Windows 7. He achieved this certification on December 01, 2015 with a certification number of F492-4448.
74 409 server virtualization with windows server hyper-v and system centerayman diab
Satya Nadella, CEO of Microsoft, certified AYMAN MOHAMED MANSOUR as a Microsoft Specialist in Server Virtualization with Windows Server Hyper-V and System Center on March 13, 2014. MANSOUR completed the requirements and was issued certification number E750-7285 to be recognized as a Microsoft Specialist in this area.
Microsoft Certified System Administrator (MCSA)ayman diab
Satya Nadella is the Chief Executive Officer of Microsoft. Ayman Mohamed Mansour has successfully completed the requirements to be recognized as a Microsoft Certified Solutions Associate: Windows Server 2008. He achieved this certification on April 16, 2012 with certification number D719-8215.