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Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library /
Take 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 st...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Apress,
2021.
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Beginning Apache Spark 3 : |b with DataFrame, Spark SQL, structured streaming, and Spark machine learning library / |c Hien Luu. |
| 250 | |a Second edition. | ||
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| 505 | 0 | |a Intro -- 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 | |
| 505 | 8 | |a Spark 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 | |
| 505 | 8 | |a Create 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 | |
| 505 | 8 | |a Working 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 | |
| 505 | 8 | |a Describe(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 | |
| 500 | |a Includes index. | ||
| 520 | |a Take 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. | ||
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