Learning Spark: Lightning-Fast Big Data Analysis"O'Reilly Media, Inc.", 28. 1. 2015. - 276 страница 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. You’ll 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.
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Садржај
Chapter 1 Introduction to Data Analysis with Spark | 1 |
Chapter 2 Downloading Spark and Getting Started | 9 |
Chapter 3 Programming with RDDs | 23 |
Chapter 4 Working with KeyValue Pairs | 47 |
Chapter 5 Loading and Saving Your Data | 71 |
Chapter 6 Advanced Spark Programming | 99 |
Chapter 7 Running on a Cluster | 117 |
Chapter 8 Tuning and Debugging Spark | 141 |
Chapter 9 Spark SQL | 161 |
Chapter 10 Spark Streaming | 185 |
Chapter 11 Machine Learning with MLlib | 215 |
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About the Authors | 257 |
Друга издања - Прикажи све
Learning Spark: Lightning-Fast Big Data Analysis Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia Ограничен приказ - 2015 |
Learning Spark: Lightning-Fast Big Data Analytics Mark Hamstra,Holden Karau,Matei Zaharia,Andy Konwinski,Patrick Wendell Приказ није доступан - 2015 |
Чести термини и фразе
algorithms Amazon S3 Apache AvgCount batch Boolean cache call signs call(String chapter Collaborative Filtering common compute conf configuration cores count create data scientists DataFrame datasets default driver program elements execution executors filesystem filter format function Hadoop HashingTF HDFS Hive HiveContext implement import input Integer integer nullable iterations Java and Scala JavaRDD<String JDBC JDBC server JSON LabeledPoint launch Lazy Evaluation lines load machine learning MapReduce Maven memory Mesos method MLlib mode multiple NumPy object operations options output package PageRank pair RDD parallel parameters partitioner partitions pipeline provides Python query reduceByKey result run Spark Scala val script SequenceFiles Serializable serialization shown in Example Spark application Spark SQL Spark Streaming spark-submit SparkConf SparkContext storage String Table tasks text file TF-IDF tion transformations tweets variable vectors worker nodes YARN