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SQL Server 2019 Revealed
29,99 € *
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Get up to speed on the game-changing developments in SQL Server 2019. No longer just a database engine, SQL Server 2019 is cutting edge with support for machine learning (ML), big data analytics, Linux, containers, Kubernetes, Java, and data virtualization to Azure. This is not a book on traditional database administration for SQL Server. It focuses on all that is new for one of the most successful modernized data platforms in the industry. It is a book for data professionals who already know the fundamentals of SQL Server and want to up their game by building their skills in some of the hottest new areas in technology. SQL Server 2019 Revealed begins with a look at the project's team goal to integrate the world of big data with SQL Server into a major product release. The book then dives into the details of key new capabilities in SQL Server 2019 using a "learn by example" approach for Intelligent Performance, security, mission-critical availability, and features for the modern developer. Also covered are enhancements to SQL Server 2019 for Linux and gain a comprehensive look at SQL Server using containers and Kubernetes clusters. The book concludes by showing you how to virtualize your data access with Polybase to Oracle, MongoDB, Hadoop, and Azure, allowing you to reduce the need for expensive extract, transform, and load (ETL) applications. You will then learn how to take your knowledge of containers, Kubernetes, and Polybase to build a comprehensive solution called Big Data Clusters, which is a marquee feature of 2019. You will also learn how to gain access to Spark, SQL Server, and HDFS to build intelligence over your own data lake and deploy end-to-end machine learning applications. What You Will Learn Implement Big Data Clusters with SQL Server, Spark, and HDFS Create a Data Hub with connections to Oracle, Azure, Hadoop, and other sources Combine SQL and Spark to build a machine learning platform for AI applications Boost your performance with no application changes using Intelligent Performance Increase security of your SQL Server through Secure Enclaves and Data Classification Maximize database uptime through online indexing and Accelerated Database Recovery Build new modern applications with Graph, ML Services, and T-SQL Extensibility with Java Improve your ability to deploy SQL Server on Linux Gain in-depth knowledge to run SQL Server with containers and Kubernetes Know all the new database engine features for performance, usability, and diagnostics Use the latest tools and methods to migrate your database to SQL Server 2019 Apply your knowledge of SQL Server 2019 to Azure Who This Book Is For IT professionals and developers who understand the fundamentals of SQL Server and wish to focus on learning about the new, modern capabilities of SQL Server 2019. The book is for those who want to learn about SQL Server 2019 and the new Big Data Clusters and AI feature set, support for machine learning and Java, how to run SQL Server with containers and Kubernetes, and increased capabilities around Intelligent Performance, advanced security, and high availability.

Anbieter: buecher
Stand: 23.02.2020
Zum Angebot
SQL Server 2019 Revealed
29,99 € *
ggf. zzgl. Versand

Get up to speed on the game-changing developments in SQL Server 2019. No longer just a database engine, SQL Server 2019 is cutting edge with support for machine learning (ML), big data analytics, Linux, containers, Kubernetes, Java, and data virtualization to Azure. This is not a book on traditional database administration for SQL Server. It focuses on all that is new for one of the most successful modernized data platforms in the industry. It is a book for data professionals who already know the fundamentals of SQL Server and want to up their game by building their skills in some of the hottest new areas in technology. SQL Server 2019 Revealed begins with a look at the project's team goal to integrate the world of big data with SQL Server into a major product release. The book then dives into the details of key new capabilities in SQL Server 2019 using a "learn by example" approach for Intelligent Performance, security, mission-critical availability, and features for the modern developer. Also covered are enhancements to SQL Server 2019 for Linux and gain a comprehensive look at SQL Server using containers and Kubernetes clusters. The book concludes by showing you how to virtualize your data access with Polybase to Oracle, MongoDB, Hadoop, and Azure, allowing you to reduce the need for expensive extract, transform, and load (ETL) applications. You will then learn how to take your knowledge of containers, Kubernetes, and Polybase to build a comprehensive solution called Big Data Clusters, which is a marquee feature of 2019. You will also learn how to gain access to Spark, SQL Server, and HDFS to build intelligence over your own data lake and deploy end-to-end machine learning applications. What You Will Learn Implement Big Data Clusters with SQL Server, Spark, and HDFS Create a Data Hub with connections to Oracle, Azure, Hadoop, and other sources Combine SQL and Spark to build a machine learning platform for AI applications Boost your performance with no application changes using Intelligent Performance Increase security of your SQL Server through Secure Enclaves and Data Classification Maximize database uptime through online indexing and Accelerated Database Recovery Build new modern applications with Graph, ML Services, and T-SQL Extensibility with Java Improve your ability to deploy SQL Server on Linux Gain in-depth knowledge to run SQL Server with containers and Kubernetes Know all the new database engine features for performance, usability, and diagnostics Use the latest tools and methods to migrate your database to SQL Server 2019 Apply your knowledge of SQL Server 2019 to Azure Who This Book Is For IT professionals and developers who understand the fundamentals of SQL Server and wish to focus on learning about the new, modern capabilities of SQL Server 2019. The book is for those who want to learn about SQL Server 2019 and the new Big Data Clusters and AI feature set, support for machine learning and Java, how to run SQL Server with containers and Kubernetes, and increased capabilities around Intelligent Performance, advanced security, and high availability.

Anbieter: buecher
Stand: 23.02.2020
Zum Angebot
Load Balancing in Multi-Core Based Clusters
54,90 € *
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Load Balancing in Multi-Core Based Clusters ab 54.9 EURO

Anbieter: ebook.de
Stand: 23.02.2020
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Wind Interference Effect on Building using Virt...
64,90 € *
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This book aims to use latest numerical techniques of computational fluid dynamics, CFD, to estimate a more accurate wind pressure for building clusters with different heights and arrangements. With these new numerical simulations, design for wind loads become more rational and less dependent on empirical rules that may lead to serious errors when applied beyond their range. These techniques, especially virtual wind tunnel, require considerably low cost and time compared with those required for the conventional wind tunnel. In addition, they work directly with design prototype instead of a scaled-down model in the conventional wind tunnel. Two separated buildings have been simulated with the help of Building Information Modeling, BIM, which allows analysis of two building at the same time. During this simulation, the interference effects of two buildings have been examined due to wind load excitation.

Anbieter: Dodax
Stand: 23.02.2020
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Channel Assignment and Security in Multi-Channe...
48,80 € *
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Wireless Mesh Networks (WMNs) are a special kind of ad hoc networks in which most of the nodes are static. Different from ad hoc networks, a mesh network introduces a hierarchy in the network architecture and provides broadband wireless Internet access to users. Recent studies have shown that nodes in a WMN are equipped with several radio interfaces for better performance. The aggregate capacity of WMNs can be significantly improved by providing each node with several Wireless Network Interface Cards (WNICs) and by using multiple channels. This is in order to minimize interference and to provide high performance. However, multiple WNICs in each node require a channel assignment planning. The channels have to be assigned in such a way that interferences decrease and the performance increases at the same time. Since the number of available channels is limited, it is desired to dynamically allocate channels on demand.In this research, we address the problem of assigning channels to nodes in WMNs. For this purpose, we introduce the Distributed Cluster Channel Assignment (CCA) algorithm with the objective of reducing network interferences to increase the overall performance of the network. This clustering approach is employed in order to simplify the method of solving the channel assignment problem in terms of complexity. One of the advantages of this approach is that the possibility to reuse the channels in different clusters. In addition, a dynamic channel assignment is proposed for the aforementioned problem. This approach is called Neighborhood Nodes Collaboration to Support QoS (NNCQ). NNCQ is adaptive to the load in WMNs and supports Quality of Service (QoS) routing. The algorithm adds or selects a channel for heavily loaded nodes based on the local information of the neighbor nodes. The selected or added channel minimizes interferences and ensures network connectivity.

Anbieter: Dodax
Stand: 23.02.2020
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Parallel Object Oriented Simulation with Lagran...
45,80 € *
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For applications that involve non-continuous matter, such as granulates, or free surface fluid flows, meshbased discretization methods are hardly applicable and more appropriate methods are required. Whenever a simulated matter disjoints or if its shape is a-priori unknown, or in case of large deformations or rotations, meshfree particle methods can be superior to their meshbased relatives. In this work two specimens of the ample field of Lagrangian meshfree particle methods are investigated with respect to their applicability in mechanical engineering. As a representative of a group of methods that allow for the simulation of disjoint solids, the Discrete Element Method is selected. The other method, Smoothed Particle Hydrodynamics, is mainly used for the simulation of fluids, but recently it has revealed its strengths also as a means to simulate visco-elastic-plastic solids.Simulations with meshfree particle methods are highly demanding in terms of CPU and memory consumption. The simulation of millions of particles is only possible if simulation programs are parallelized. This requires the respective algorithms to be modified in a way that makes them suitable for an efficient use in parallel environments. The volatility of particle interactions thereby requires special techniques to guarantee a homogeneous work distribution, so called dynamic load balancing. In this thesis a new hierarchical controller based load balancing scheme is presented that is designed to be used in multi-user environments, such as university clusters. The viability of the approach is demonstated by means of several benchmark simulations.

Anbieter: Dodax
Stand: 23.02.2020
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An Investigation into User Text Query and Text ...
79,00 € *
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Cognitive limitations such as those described in Miller's (1956) work on channel capacity and Cowen's (2001) on short-term memory are factors in describing user cognitive load and in turn task performance. Inappropriate user cognitive load can reduce user efficiency in goal realization and so in the task of text search the interface should allow users to draw on their powerful and innate pattern recognition abilities. This harmonizes with Johnson-Laird's (1983) proposal that propositional representation is tied to mental models. An appropriate approach would be to graphically present single documents, or clusters thereof, with an appropriate number and type of descriptors. However, a lack of basic research into human preference regarding query formation and the heuristics users employ in search extends to features as elementary as the number of words preferred to describe and/or search for a document. Understanding these preferences will help balance processing overheads of tasks like clustering against user cognitive load.

Anbieter: Dodax
Stand: 23.02.2020
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Analysis of Static Recovery Schemes for Distrib...
49,00 € *
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Availability is a key feature in the fault tolerant distributed systems, like clusters, and it can be achieved by using failover techniques. For cluster availability, simplified strategies such as cold backup and warm backup are used by spare resources. High cost can be a drawback for using many stand-by computers in a large cluster system in order to achieve predefined level of availability. Another substitute solution for failure detection and recovery is the hot backup, even it is hard to make a decision on which computer the task of failure computer is executed while maintaining the load balance. Dynamic monitoring facilities and central scheduling are the usual solutions for this. In practice, the above solution turned out to be a problem with fault tolerant and scalability, while the mandatory scheduler acts both as a single point of failure and coordination bottleneck.

Anbieter: Dodax
Stand: 23.02.2020
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Clustering in Wireless Sensor Networks
49,00 € *
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Energy constraint is the most critical problem in wireless sensor networks. To address this issue, clustering has been introduced as an efficient way for routing. However, the available clustering algorithms do not efficiently consider the geographical information of nodes in cluster-head election. This leads to uneven distribution of cluster-heads and unbalanced cluster sizes that brings about uneven energy dissipation in clusters. In this book, an Efficient Distributed Cluster-head Election technique for Load balancing (EDCEL) is proposed. The main criterion of the algorithm, dispersal of cluster-heads, is achieved by increasing the Euclidean distance between cluster-heads. Simulation results show the effectiveness of this approach in terms of balancing intra-cluster energy dissipation and lifetime longevity.

Anbieter: Dodax
Stand: 23.02.2020
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