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.
|
Шта други кажу - Напишите рецензију
Нисмо пронашли ниједну рецензију на уобичајеним местима.
Садржај
1. одељак | |
2. одељак | |
3. одељак | |
4. одељак | |
5. одељак | |
6. одељак | |
7. одељак | |
8. одељак | |
12. одељак | |
13. одељак | |
14. одељак | |
15. одељак | |
16. одељак | |
17. одељак | |
18. одељак | |
19. одељак | |
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
accumulators Aggregations algorithms Amazon S3 Apache Hive AvgCount batch Benefit from Partitioning cache Cassandra chapter Collaborative Filtering Components of Execution compute configuration count create data scientists DataFrames datasets default driver program elements executors fault tolerance filesystem filter function Hadoop Hadoop input HDFS HiveContext import Integer JDBC JSON launch level of parallelism Linear regression Loading CSV Logistic regression machine learning MapReduce Maven memory MLlib mode multiple nullable NumPy object files options Output Operations package PageRank pair RDDs parameters partitioner PerPartition pipeline Python Python and Scala query random forests reduceByKey regression result Saving Scala Shells Scala val scheduling script SequenceFiles serialization shown in Example spam Spark Application Built Spark SQL Spark Streaming spark-submit SparkConf SparkContext Standalone cluster manager Stateless Transformations storage String tasks text files tweets UDFs vectors worker nodes