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Titlebook: SQL Server Big Data Clusters; Data Virtualization, Benjamin Weissman,Enrico van de Laar Book 2020Latest edition Benjamin Weissman and Enric

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發(fā)表于 2025-3-21 19:50:38 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱SQL Server Big Data Clusters
副標(biāo)題Data Virtualization,
編輯Benjamin Weissman,Enrico van de Laar
視頻videohttp://file.papertrans.cn/861/860298/860298.mp4
概述Introduces the marquee feature in SQL Server 2019 for combining HDFS and relational data.Explains the pros and cons of data virtualization versus data integration (PolyBase vs SSIS).Shows the use of K
圖書封面Titlebook: SQL Server Big Data Clusters; Data Virtualization, Benjamin Weissman,Enrico van de Laar Book 2020Latest edition Benjamin Weissman and Enric
描述Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to?use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database.?.Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from
出版日期Book 2020Latest edition
關(guān)鍵詞SQL Server; SQL Server 2019; Apache Spark; Machine Learning; Kubernetes; PolyBase; Linux; Docker; Big Data; D
版次2
doihttps://doi.org/10.1007/978-1-4842-5985-6
isbn_softcover978-1-4842-5984-9
isbn_ebook978-1-4842-5985-6
copyrightBenjamin Weissman and Enrico van de Laar 2020
The information of publication is updating

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Create and Consume Big Data Cluster Apps,lication, or app as we will call it in the remainder of this chapter, to perform various maintenance tasks on top of your data like a database backup. Another example is the ability to create an entry point for your machine learning processes through a REST API, a use case which we will explore later in this chapter.
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發(fā)表于 2025-3-22 11:04:33 | 只看該作者
Working with Spark in Big Data Clusters,ailable to query data that is stored inside the HDFS filesystem of your Big Data Cluster. As you have read in Chapter 2, Big Data Clusters also have Spark included in the architecture, meaning we can leverage the power of Spark to query data stored inside our Big Data Cluster.
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發(fā)表于 2025-3-22 13:39:12 | 只看該作者
Machine Learning on Big Data Clusters,e of having access to data stored in different formats is that it allows you to perform analysis of the data at a large, and distributed, scale. One of the more powerful options we can utilize inside Big Data Clusters is the ability to implement machine learning solutions on our data. Because Big Da
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Create and Consume Big Data Cluster Apps,werful feature, since it allows you to script and run a wide variety of solutions on top of your Big Data Cluster. For instance, you can create an application, or app as we will call it in the remainder of this chapter, to perform various maintenance tasks on top of your data like a database backup.
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978-1-4842-5984-9Benjamin Weissman and Enrico van de Laar 2020
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發(fā)表于 2025-3-23 08:29:42 | 只看該作者
Big Data Cluster Architecture,SQL Server Big Data Clusters are made up from a variety of technologies all working together to create a centralized, distributed data environment. In this chapter, we are going to look at the various technologies that make up Big Data Clusters through two different views.
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