asdfThis document examines the role of big data in the enterprise as it relates to network design considerations. It describes the rise of big data and the transition of traditional enterprise data models with the addition of crucial building blocks to handle the dramatic growth of data in the enterprise. According to IDC estimates, the size of the "digital universe" in 2011 will be 1.8 zettabytes (1.8 trillion gigabytes). With information growth exceeding Moore's Law, the average enterprise will need to manage 50 times more information by the year 2020 while increasing IT staff by only 1.5 percent. With this challenge in mind, the integration of big data models into existing enterprise infrastructures is a critical element when considering the addition of new big data building blocks while considering the efficiency, economics and privacy. This document also shows that the Cisco Nexus ® architectures are optimized to handle big data while providing integration into current enterprise infrastructures.
In reviewing multiple data models, this document examines the effects of Apache Hadoop as a building block for big data and its effects on the network. Hadoop is an open source software platform for building reliable, scalable clusters in a scaled-out, "shared-nothing" design model for storing, processing, and analyzing enormous volumes of data at very high performance.
The information presented in this document is based on the actual network traffic patterns of the Hadoop framework and can help in the design of a scalable network with the right balance of technologies that actually contribute to the application's network performance. Understanding the application's traffic patterns fosters collaboration between the application and network design teams, allowing advancements in technologies that enhance application performance.
Note: Although this document omits most programming details, many excellent publications about Hadoop applications are available, such as Hadoop: The Definitive Guide, Second Edition, which is referenced in this document.

Emergence of Big Data
Big data is a foundational element of social networking and Web 2.0-based information companies. The enormous amount of data is generated as a result of democratization and ecosystem factors such as the following:
• Mobility trends: Mobile devices, mobile events and sharing, and sensory integration
• Data access and consumption: Internet, interconnected systems, social networking, and convergent interfaces and access models (Internet, search and social networking, and messaging)
• Ecosystem capabilities: Major changes in the information processing model and the availability of an open source framework; the general-purpose computing and unified network integration
Data generation, consumption, and analytics have provided competitive business advantages for Web 2.0 portals and Internet-centric firms that offer services to customers and services differentiation through correlation of adjacent data. (An IDC data study provides a compelling view of the future of data growth; see http://idcdocserv.com/1142.)
With the rise of business intelligence data mining and analytics spanning market research, behavioral modeling, and inference-based decision, data can be used to provide a competitive advantage. Here are just a few of the nearly limitless use cases of big data for the companies with large Internet presence:
• Targeted marketing and advertising
• Related attached sale promotions
• Analysis of behavioral social patterns
• Metadata-based optimization of workload and performance management for millions of users
Big Data Moves into the Enterprise
The requirements of traditional enterprise data models for application, database, and storage resources have grown over the years, and the cost and complexity of these models has increased along the way to meet the needs of big data. This rapid change has prompted changes in the fundamental models that describe the way that big data is stored, analyzed, and accessed. The new models are based on a scaled-out, shared-nothing architecture, bringing new challenges to enterprises to decide what technologies to use, where to use them, and how. One size no longer fits all, and the traditional model is now being expanded to incorporate new building blocks that address the challenges of big data with new information processing frameworks purpose-built to meet big data's requirements. However, these purpose-built systems also must meet the inherent requirement for integration into current business models, data strategies, and network infrastructures.
Big Data Components

This document examines the role of big data in the enterprise as it relates to network design considerations. It describes the rise of big data and the transition of traditional enterprise data models with the addition of crucial building blocks to handle the dramatic growth of data in the enterprise. According to IDC estimates, the size of the "digital universe" in 2011 will be 1.8 zettabytes (1.8 trillion gigabytes). With information growth exceeding Moore's Law, the average enterprise will need to manage 50 times more information by the year 2020 while increasing IT staff by only 1.5 percent. With this challenge in mind, the integration of big data models into existing enterprise infrastructures is a critical element when considering the addition of new big data building blocks while considering the efficiency, economics and privacy. This document also shows that the Cisco Nexus ® architectures are optimized to handle big data while providing integration into current enterprise infrastructures.
In reviewing multiple data models, this document examines the effects of Apache Hadoop as a building block for big data and its effects on the network. Hadoop is an open source software platform for building reliable, scalable clusters in a scaled-out, "shared-nothing" design model for storing, processing, and analyzing enormous volumes of data at very high performance.
The information presented in this document is based on the actual network traffic patterns of the Hadoop framework and can help in the design of a scalable network with the right balance of technologies that actually contribute to the application's network performance. Understanding the application's traffic patterns fosters collaboration between the application and network design teams, allowing advancements in technologies that enhance application performance.
Note: Although this document omits most programming details, many excellent publications about Hadoop applications are available, such as Hadoop: The Definitive Guide, Second Edition, which is referenced in this document.

Emergence of Big Data
Big data is a foundational element of social networking and Web 2.0-based information companies. The enormous amount of data is generated as a result of democratization and ecosystem factors such as the following:
• Mobility trends: Mobile devices, mobile events and sharing, and sensory integration
• Data access and consumption: Internet, interconnected systems, social networking, and convergent interfaces and access models (Internet, search and social networking, and messaging)
• Ecosystem capabilities: Major changes in the information processing model and the availability of an open source framework; the general-purpose computing and unified network integration
Data generation, consumption, and analytics have provided competitive business advantages for Web 2.0 portals and Internet-centric firms that offer services to customers and services differentiation through correlation of adjacent data. (An IDC data study provides a compelling view of the future of data growth; see http://idcdocserv.com/1142.)
With the rise of business intelligence data mining and analytics spanning market research, behavioral modeling, and inference-based decision, data can be used to provide a competitive advantage. Here are just a few of the nearly limitless use cases of big data for the companies with large Internet presence:
• Targeted marketing and advertising
• Related attached sale promotions
• Analysis of behavioral social patterns
• Metadata-based optimization of workload and performance management for millions of users
Big Data Moves into the Enterprise
The requirements of traditional enterprise data models for application, database, and storage resources have grown over the years, and the cost and complexity of these models has increased along the way to meet the needs of big data. This rapid change has prompted changes in the fundamental models that describe the way that big data is stored, analyzed, and accessed. The new models are based on a scaled-out, shared-nothing architecture, bringing new challenges to enterprises to decide what technologies to use, where to use them, and how. One size no longer fits all, and the traditional model is now being expanded to incorporate new building blocks that address the challenges of big data with new information processing frameworks purpose-built to meet big data's requirements. However, these purpose-built systems also must meet the inherent requirement for integration into current business models, data strategies, and network infrastructures.
Big Data Components