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HAWAII STATE PUBLIC LIBRARY SYSTEM
Item Information
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Summary
More Content
More by this author
Chambers, Bill (William Andrew), author.
Subjects
Spark (Electronic resource : Apache Software Foundation)
Apache (Computer file : Apache Group)
Information retrieval.
Big data.
Data mining -- Computer programs.
Electronic data processing.
Telecommunication -- Message processing.
Web servers -- Computer programs.
Web applications -- Development.
Browse Catalog
by author:
Chambers, Bill (William Andrew), author.
by title:
Spark : the definiti...
MARC Display
Spark : the definitive guide : big data processing made simple / Bill Chambers and Matei Zaharia.
by
Chambers, Bill (William Andrew), author.
Sebastapol, CA : O'Reilly Media, 2018.
Subjects
Spark (Electronic resource : Apache Software Foundation)
Apache (Computer file : Apache Group)
Information retrieval.
Big data.
Data mining -- Computer programs.
Electronic data processing.
Telecommunication -- Message processing.
Web servers -- Computer programs.
Web applications -- Development.
ISBN:
9781491912218 (pbk.) :
Description:
xxvi, 576 pages : illustrations ; 24 cm.
Edition:
First edition.
Contents:
Part 1. Gentle overview of big data and Spark. What is Apache Spark? -- A gentle introduction to Spark -- A tour of Spark's toolset -- Part 2. Structured APIs : DataFrames, SQL, and datasets. Structured API overview -- Basic structured operations -- Working with different types of data -- Aggregations -- Joins -- Data sources -- Spark SQL -- Datasets -- Part 3. Low-level APIs. Resilient distributed datasets (RDDs) -- Advanced RDDs -- Distributed shared variables -- Part 4. Production applications. How Spark runs on a cluster -- Developint Spark applications -- Deploying Spark -- Monitoring and debugging -- Performance tuning -- Part 5. Streaming. Stream processing fundamentals -- Structured streaming basics -- Event-time and stateful processing -- Structured streaming in production -- Part 6. Advanced analytics and machine learning. Advanced analytics and machine learning overview -- Preprocessing and feature engineering -- Classification -- Regression -- Recommendation -- Unsupervised learning -- Graph analytics -- Deep learning -- Part 7. Ecosystem. Language specifics : Python (PySpark) and R (SparkR and sparklyr) -- Ecosystem and community.
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Call No.
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Hawaii State Library
Business, Science & Technology
006.312 Ch
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