001440538 000__ 06772cam\a2200625Ia\4500
001440538 001__ 1440538
001440538 003__ OCoLC
001440538 005__ 20230309004610.0
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001440538 019__ $$a1280603550$$a1281140102$$a1281974616$$a1283844625$$a1284805767$$a1295599731$$a1306588771
001440538 020__ $$a9781484273838$$q(electronic bk.)
001440538 020__ $$a1484273834$$q(electronic bk.)
001440538 020__ $$z1484273826
001440538 020__ $$z9781484273821
001440538 0247_ $$a10.1007/978-1-4842-7383-8$$2doi
001440538 035__ $$aSP(OCoLC)1280460608
001440538 040__ $$aYDX$$beng$$cYDX$$dSTF$$dZ5A$$dEBLCP$$dTOH$$dORMDA$$dOCLCF$$dGW5XE$$dCZL$$dOCLCO$$dN$T$$dOCL$$dUKAHL$$dOCLCQ
001440538 049__ $$aISEA
001440538 050_4 $$aQA76.9.D3
001440538 08204 $$a005.7$$223
001440538 1001_ $$aLuu, Hien,$$eauthor.
001440538 24510 $$aBeginning Apache Spark 3 :$$bwith DataFrame, Spark SQL, structured streaming, and Spark machine learning library /$$cHien Luu.
001440538 250__ $$aSecond edition.
001440538 264_1 $$aNew York :$$bApress,$$c2021.
001440538 300__ $$a1 online resource
001440538 336__ $$atext$$btxt$$2rdacontent
001440538 337__ $$acomputer$$bc$$2rdamedia
001440538 338__ $$aonline resource$$bcr$$2rdacarrier
001440538 500__ $$aIncludes index.
001440538 5050_ $$aIntro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Apache Spark -- Overview -- History -- Spark Core Concepts and Architecture -- Spark Cluster and Resource Management System -- Spark Applications -- Spark Drivers and Executors -- Spark Unified Stack -- Spark Core -- Spark SQL -- Spark Structured Streaming -- Spark MLlib -- Spark GraphX -- SparkR -- Apache Spark 3.0 -- Adaptive Query Execution Framework -- Dynamic Partition Pruning (DPP) -- Accelerator-aware Scheduler -- Apache Spark Applications
001440538 5058_ $$aSpark Example Applications -- Apache Spark Ecosystem -- Delta Lake -- Koalas -- MLflow -- Summary -- Chapter 2: Working with Apache Spark -- Downloading and Installation -- Downloading Spark -- Installing Spark -- Spark Scala Shell -- Spark Python Shell -- Having Fun with the Spark Scala Shell -- Useful Spark Scala Shell Command and Tips -- Basic Interactions with Scala and Spark -- Basic Interactions with Scala -- Spark UI and Basic Interactions with Spark -- Spark UI -- Basic Interactions with Spark -- Introduction to Collaborative Notebooks -- Create a Cluster -- Create a Folder
001440538 5058_ $$aCreate a Notebook -- Setting up Spark Source Code -- Summary -- Chapter 3: Spark SQL: Foundation -- Understanding RDD -- Introduction to the DataFrame API -- Creating a DataFrame -- Creating a DataFrame from RDD -- Creating a DataFrame from a Range of Numbers -- Creating a DataFrame from Data Sources -- Creating a DataFrame by Reading Text Files -- Creating a DataFrame by Reading CSV Files -- Creating a DataFrame by Reading JSON Files -- Creating a DataFrame by Reading Parquet Files -- Creating a DataFrame by Reading ORC Files -- Creating a DataFrame from JDBC
001440538 5058_ $$aWorking with Structured Operations -- Working with Columns -- Working with Structured Transformations -- select(columns) -- selectExpr(expressions) -- filler(condition), where(condition) -- distinct, dropDuplicates -- sort(columns), orderBy(columns) -- limit(n) -- union(otherDataFrame) -- withColumn(colName, column) -- withColumnRenamed(existingColName, newColName) -- drop(columnName1, columnName2) -- sample(fraction), sample(fraction, seed), sample(fraction, seed, withReplacement) -- randomSplit(weights) -- Working with Missing or Bad Data -- Working with Structured Actions
001440538 5058_ $$aDescribe(columnNames) -- Introduction to Datasets -- Creating Datasets -- Working with Datasets -- Using SQL in Spark SQL -- Running SQL in Spark -- Writing Data Out to Storage Systems -- The Trio: DataFrame, Dataset, and SQL -- DataFrame Persistence -- Summary -- Chapter 4: Spark SQL: Advanced -- Aggregations -- Aggregation Functions -- Common Aggregation Functions -- count(col) -- countDistinct(col) -- min(col), max(col) -- sum(col) -- sumDistinct(col) -- avg(col) -- skewness(col), kurtosis(col) -- variance(col), stddev(col) -- Aggregation with Grouping -- Multiple Aggregations per Group
001440538 506__ $$aAccess limited to authorized users.
001440538 520__ $$aTake a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section. After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications. What You Will Learn Master the Spark unified data analytics engine and its various components Work in tandem to provide a scalable, fault tolerant and performant data processing engine Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL Develop machine learning applications using Spark MLlib Manage the machine learning development lifecycle using MLflow Who This Book Is For Data scientists, data engineers and software developers.
001440538 63000 $$aSpark (Electronic resource : Apache Software Foundation)
001440538 650_0 $$aBig data.
001440538 650_0 $$aDistributed databases.
001440538 650_0 $$aOpen source software.
001440538 650_0 $$aMachine learning.
001440538 650_6 $$aDonnées volumineuses.
001440538 650_6 $$aBases de données réparties.
001440538 650_6 $$aLogiciels libres.
001440538 650_6 $$aApprentissage automatique.
001440538 655_0 $$aElectronic books.
001440538 77608 $$iPrint version: $$z1484273826$$z9781484273821$$w(OCoLC)1262191908
001440538 852__ $$bebk
001440538 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7383-8$$zOnline Access$$91397441.1
001440538 909CO $$ooai:library.usi.edu:1440538$$pGLOBAL_SET
001440538 980__ $$aBIB
001440538 980__ $$aEBOOK
001440538 982__ $$aEbook
001440538 983__ $$aOnline
001440538 994__ $$a92$$bISE