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PySpark Cookbook

efficiently combining Python and Apache Spark to process data Book DescriptionApache Spark is an open source framework for efficient cluster computing;

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Apache Spark for Data Science Cookbook

Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book * Use Apache Spark for data processing;

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Apache Spark 2

and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the;

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Apache Spark 2.x Cookbook

This book is for data engineers, data scientists, and those who want to implement Spark for real-time data processing. Anyone who is using;

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Modern Data Engineering with Apache Spark

workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book;

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Beginning Apache Spark 2

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable;

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Big Data Analytics

A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop;

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Hadoop Real-World Solutions Cookbook

Book Is For Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes. What You;

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Frank Kane's Taming Big Data with Apache Spark and Python

over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who;

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Apache Spark 2.x for Java Developers

data processing with Spark-without having to learn Scala! * Use the Spark Java API to implement efficient enterprise-grade applications for;

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Spark for Data Science

with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine;

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Mastering Apache Spark 2.x -

parameter settings affect overall performance of an Apache Spark cluster * Leverage Scala, R and python for your data science projects In Detail;

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Apache Spark Quick Start Guide

A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key;

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Beginning Apache Spark 3

efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing;

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Scala Programming for Big Data Analytics

in comparison to Java, and how Scala is related to Apache Spark for big data analytics. Next, you'll set up the Scala environment ready for examining;

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Practical Apache Spark

of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You'll follow a;

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Beginning Apache Spark Using Azure Databricks

the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics;

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Mastering Spark with R The Complete Guide to LargeScale Analysis and Modeling

of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large;

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Apache Spark Machine Learning Blueprints

improve and refine your predictive models for practical implementation Who This Book Is For If you are a data scientist, a data analyst, or;

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Apache Spark Graph Processing

Build, process and analyze large-scale graph data effectively with Spark About This Book * Find solutions for every stage of data;

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Hands-On Deep Learning with Apache Spark

Who this book is forIf you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing;

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Modern Data Engineering with Apache Spark

Intermediate-Advanced user level;...

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Practical Data Analysis -

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book;

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Learning Apache Spark 2

analytics and its importance for organizations and data professionals * Delve into Spark to see how it is different from existing processing;

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Spark in Action

. Apache Spark is a big data processing framework perfect for analyzing near-real-time streams and discovering historical patterns in batched;

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Hadoop Application Architectures

the process.This book covers: Factors to consider when using Hadoop to store and model data Best practices for moving data in and out of the;

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Spark GraphX in Action

difficult and computationally expensive to explore, query, and analyze. GraphX, a powerful graph processing API for the Apache Spark analytics;

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