Enter Apache Spark. Updated to include Spark 3. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Ready to unlock the power of your data? This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
Apache Spark is amazing when everything clicks. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours.
Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time.
But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.
This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions.
And how to move all of this data becomes nearly as important as the data itself. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform.
Understand publish-subscribe messaging and how it fits in the big data ecosystem. In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source.
About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications.
About the author Jean-Georges Perrin is an experienced data and software architect. What could you do with data if scalability wasn't a problem? With this hands-on guide, you'll learn how Apache Cassandra handles hundreds of terabytes of data while remaining highly available across multiple data centers -- capabilities that have attracted Facebook, Twitter, and other data-intensive companies.
Cassandra: The Definitive Guide provides the technical details and practical examples you need to assess this database management system and put it to work in a production environment.
Author Eben Hewitt demonstrates the advantages of Cassandra's nonrelational design, and pays special attention to data modeling. If you're a developer, DBA, application architect, or manager looking to solve a database scaling issue or future-proof your application, this guide shows you how to harness Cassandra's speed and flexibility.
Understand the tenets of Cassandra's column-oriented structure Learn how to write, update, and read Cassandra data Discover how to add or remove nodes from the cluster as your application requires Examine a working application that translates from a relational model to Cassandra's data model Use examples for writing clients in Java, Python, and C Use the JMX interface to monitor a cluster's usage, memory patterns, and more Tune memory settings, data storage, and caching for better performance.
Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.
You will get familiar with the modules available in PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. This repository is currently a work in progress and new material will be added over time.
You can find the code from the book in the code subfolder where it is broken down by language and chapter. For instance, you might go to this page. Once you do that, you're going to need to navigate to the RAW version of the file and save that to your Desktop.
You can do that by clicking the Raw button. Alternatively, you could just clone the entire repository to your local desktop and navigate to the file on your computer. Read the instructions here. Simply open the Databricks workspace and go to import in a given directory. From there, navigate to the file on your computer to upload it. Unfortunately due to a recent security upgrade, notebooks cannot be imported from external URLs.
Therefore you must upload it from your computer. Now you just need to simply run the notebooks! All the examples run on Databricks Runtime 3. Once you've created your cluster, attach the notebook. Once you've done that, all examples should run without issue. You can add a Maven dependency with the following coordinates:. As new Spark releases come out for each development stream, previous ones will be archived,but they are still available at Spark release archives.
Please consult theSecurity page for a list of known issues that may affect the version you downloadbefore deciding to use it. Skip to content. Checkout here for Latest PDF's. All books are in clear copy here, and all files are secure so don't worry about it. Spark The Definitive Guide. Latest Preview Release Preview releases, as the name suggests, are releases for previewing upcoming features. Link with Spark Spark artifacts are hosted in Maven Central.
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