Top 10 best holden karau for 2019

Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, youll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. 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.

Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. Youll learn about recent changes to Hadoop, and explore new case studies on Hadoops role in healthcare systems and genomics data processing.

• Learn fundamental components such as MapReduce, HDFS, and YARN
• Explore MapReduce in depth, including steps for developing applications with it
• Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN
• Learn two data formats: Avro for data serialization and Parquet for nested data
• Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer)
• Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with Hadoop
• Learn the HBase distributed database and the ZooKeeper distributed configuration service

Apache Spark is amazing when everything clicks. But if you havent seen the performance improvements you expected, or still dont feel confident enough to use Spark in production, this practical book is for you. 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. Not only will you gain a more comprehensive understanding of Spark, youll also learn how to make it sing.

With this book, youll explore:

• How Spark SQLs new interfaces improve performance over SQLs RDD data structure
• The choice between data joins in Core Spark and Spark SQL
• Techniques for getting the most out of standard RDD transformations
• How to work around performance issues in Sparks key/value pair paradigm
• Writing high-performance Spark code without Scala or the JVM
• How to test for functionality and performance when applying suggested improvements
• Using Spark MLlib and Spark ML machine learning libraries
• Sparks Streaming components and external community packages

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. Youll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.

• Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell
• Leverage Sparks powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib
• Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm
• Learn how to deploy interactive, batch, and streaming applications
• Connect to data sources including HDFS, Hive, JSON, and S3
• Master advanced topics like data partitioning and shared variables

In the second edition of 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. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.

Youll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniquesincluding classification, clustering, collaborative filtering, and anomaly detectionto fields such as genomics, security, and finance.

If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, youll find the books patterns useful for working on your own data applications.

With this book, you will:

• Familiarize yourself with the Spark programming model
• Become comfortable within the Spark ecosystem
• Learn general approaches in data science
• Examine complete implementations that analyze large public data sets
• Discover which machine learning tools make sense for particular problems
• Acquire code that can be adapted to many uses

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.

Youll explore the basic operations and common functions of Sparks structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparks scalable machine-learning library.

• Get a gentle overview of big data and Spark
• Learn about DataFrames, SQL, and DatasetsSparks core APIsthrough worked examples
• Dive into Sparks low-level APIs, RDDs, and execution of SQL and DataFrames
• Understand how Spark runs on a cluster
• Debug, monitor, and tune Spark clusters and applications
• Learn the power of Structured Streaming, Sparks stream-processing engine
• Learn how you can apply MLlib to a variety of problems, including classification or recommendation

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.

Youll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniquesclassification, collaborative filtering, and anomaly detection among othersto fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, youll find these patterns useful for working on your own data applications.

Patterns include:

• Recommending music and the Audioscrobbler data set
• Predicting forest cover with decision trees
• Anomaly detection in network traffic with K-means clustering
• Understanding Wikipedia with Latent Semantic Analysis
• Analyzing co-occurrence networks with GraphX
• Geospatial and temporal data analysis on the New York City Taxi Trips data
• Estimating financial risk through Monte Carlo simulation
• Analyzing genomics data and the BDG project
• Analyzing neuroimaging data with PySpark and Thunder