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Pro Spark Streaming

unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the;

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Big Data Analytics

commonly used tools. * Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming;

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Spark - The Definitive Guide

a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark's stream-processing;

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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

stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you'll learn the fundamentals of Spark ML for;

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Apache Spark 2.x Cookbook

Spark including Databricks Cloud notebook * Find out how to operate on data in Spark with schemas * Get to grips with real-time streaming;

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Learning Spark

learning. How can you process such varied data workloads efficiently? Enter Apache Spark. Updated to emphasize new features in Spark 2.4., this;

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Learning Spark

powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools;

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Apache Spark 2

querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and;

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Learning Spark SQL

Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API;

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Spark in Action

near-real time data with Spark Streaming, how to apply machine learning algorithms with Spark MLlib, how to apply graph algorithms on;

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Apache Spark 2.x for Java Developers

text, using the Spark core Library. * Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming;

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Scala and Spark for Big Data Analytics

structured and unstructured data using SparkSQL and GraphX * Scalable and fault-tolerant streaming application development using Spark structured;

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Apache Spark Quick Start Guide

architecture and the execution flow of a Spark application Explore built-in modules for SQL, streaming, ML, and graph analysis Optimize your Spark job;

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Beginning Apache Spark 3

Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and;

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Spark in Action, Second Edition

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any;

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Scala Data Analysis Cookbook

variety of cluster managers * Run streaming and graph analytics in Spark to visualize data, enabling exploratory analysis In Detail This;

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Twitch For Dummies

sessions, and even pro sports. Twitch For Dummies helps initiate those new to streaming with advice on how to launch and build a Twitch channel;

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Practical Apache Spark

various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help;

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Mastering Spark for Data Science

core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be;

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Mastering Apache Spark 2.x -

Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how;

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Professional Spark

guidance on resource scheduling, db connectors, streaming, security, and much more. Spark has become the tool of choice for many Big Data problems;

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Building Data Streaming Applications with Apache Kafka

streaming applications with Kafka using Spark, Storm &, and Heron * Understand the importance of a low -latency , high- throughput, and fault;

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Streaming Data

DESCRIPTION Many of the technologies discussed in the book-Spark, Storm, Kafka, Impala, RabbitMQ, etc.-are covered individually;

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Learn PySpark

techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms;

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Learn PySpark

PySpark fundamentals, such as Sparks core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and;

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

Spark using the MLlib library * Process continuous streams of data in real time using the Spark streaming module * Perform complex network;

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