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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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Practical Apache Spark

Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts;

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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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Beginning Apache Spark 2

machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows;

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Beginning Apache Spark 3

using dataframe and Spark SQL Develop machine learning applications using Spark MLlib Manage the machine learning development lifecycle using;

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Apache Spark 2.x for Java Developers

SQL Library * Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems * Get to know Spark GraphX;

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Spark - The Definitive Guide

, Spark's scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets-Spark's;

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Apache Spark Quick Start Guide

built-in modules for SQL, Streaming, Machine Learning and Graph analysis Get introduced to a variety of optimizations based on the actual;

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Big Data Analytics

Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx * See;

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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

learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark's;

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Spark in Action

data sets. But Spark goes much further than other frameworks. By including machine learning and graph processing capabilities, it makes;

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Spark in Action, Second Edition

applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists;

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Mastering Apache Spark 2.x -

, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand;

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Beginning Azure Synapse Analytics

analyze your big data using Serverless Synapse SQL Pool. You also will learn Synapse Spark and Synapse Pipelines, with examples. And you will;

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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 Machine Learning with Spark 2.x

Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial;

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Learning PySpark

* Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0 * Develop and deploy;

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Modern Data Engineering with Apache Spark

Simplify data transformation with Spark Pipelines and Spark SQL Bridge data engineering with machine learning Architect modular data pipeline;

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Optimizing Databricks Workloads

engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial.;

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Beginning Apache Spark Using Azure Databricks

, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what;

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Big Data Analytics with Spark

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster;

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PolyBase Revealed

2019 to integrate SQL Server with Azure Blob Storage, Apache Hadoop, other SQL Server instances, Oracle, Cosmos DB, Apache Spark, and more. You;

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Fast Data Processing with Spark 2 - Third Edition

DataFrame APIs * Get to know the different ways to interact with Spark's distributed representation of data (RDDs) * Query Spark with a SQL-like;

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Learning SQL on SQL Server 2005

Provides information on SQL concepts, covering such topics as SQL commands, joins, functions, query development, set operations, and;

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Learning Oracle SQL and PL/SQL

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Learning Apache Spark 2

, MESOS or a Stand-alone cluster manager. * Learn the concepts of Spark SQL, SchemaRDD, Caching and working with Hive and Parquet file formats;

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