Distributed Storage Networks
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Distributed Storage Networks
Author: Thomas C. Jepsen
language: en
Publisher: John Wiley & Sons
Release Date: 2003-11-21
The worldwide market for SAN and NAS storage is anticipated to grow from US $2 billion in 1999 to over $25 billion by 2004. As business-to-business and business-to-consumer e-commerce matures, even greater demands for management of stored data will arise. With the rapid increase in data storage requirements in the last decade, efficient management of stored data becomes a necessity for the enterprise. A recent UC-Berkeley study predicts that 150,000 terabytes of disk storage will be shipped in 2003. Most financial, insurance, healthcare, and telecommunications institutions are in the process of implementing storage networks that are distributed to some degree. For these institutions, data integrity is critical, and they will spend much time and money on planning. One of the primary obstacles to implementing a storage network cited by enterprise IT managers is a lack of knowledge about storage networking technology and the specific issues involved in extending a Storage Area Network (SAN) or Network Attached Storage (NAS) over the Metropolitan Area Networks (MAN) or Wireless Area Networks (WAN). Distributed Storage Networks: Architecture, Protocols and Management addresses the "terminology gap" between enterprise network planners and telecommunications engineers, who must understand the transport requirements of storage networks in order to implement distributed storage networks. Jepsen comprehensively provides IT managers, planners, and telecommunications professionals with the information they need in order to choose the technologies best suited for their particular environment. Addresses a hot topic that will become increasingly important in the coming years Enables high-level managers and planners to make intelligent decisions about network needs Includes example network configurations providing solutions to typical user scenarios Fills the "terminology gap" between enterprise network managers and telecommunications engineers who must understand the transport requirements of storage networks in order to implement distributed storage area networks A fundamental resource for all network managers, planners and network design engineers, as well as telecommunications engineers and engineering, computer science, and information technology students.
Distributed Storage Networks
Author: Thomas C. Jepsen
language: en
Publisher: John Wiley & Sons
Release Date: 2013-05-28
The worldwide market for SAN and NAS storage is anticipated to grow from US $2 billion in 1999 to over $25 billion by 2004. As business-to-business and business-to-consumer e-commerce matures, even greater demands for management of stored data will arise. With the rapid increase in data storage requirements in the last decade, efficient management of stored data becomes a necessity for the enterprise. A recent UC-Berkeley study predicts that 150,000 terabytes of disk storage will be shipped in 2003. Most financial, insurance, healthcare, and telecommunications institutions are in the process of implementing storage networks that are distributed to some degree. For these institutions, data integrity is critical, and they will spend much time and money on planning. One of the primary obstacles to implementing a storage network cited by enterprise IT managers is a lack of knowledge about storage networking technology and the specific issues involved in extending a Storage Area Network (SAN) or Network Attached Storage (NAS) over the Metropolitan Area Networks (MAN) or Wireless Area Networks (WAN). Distributed Storage Networks : Architecture, Protocols and Management addresses the "terminology gap" between enterprise network planners and telecommunications engineers, who must understand the transport requirements of storage networks in order to implement distributed storage networks. Jepsen comprehensively provides IT managers, planners, and telecommunications professionals with the information they need in order to choose the technologies best suited for their particular environment. * Addresses a hot topic that will become increasingly important in the coming years * Enables high-level managers and planners to make intelligent decisions about network needs. * Includes example network configurations providing solutions to typical user scenarios * Fills the "terminology gap" between enterprise network managers and telecommunications engineers who must understand the transport requirements of storage networks in order to implement distributed storage area networks A fundamental resource for all network managers, planners and network design engineers, as well as telecommunications engineers and engineering, computer science, and information technology students.
Foundations for Architecting Data Solutions
Author: Ted Malaska
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-08-29
While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect