| Title : | Spark : the definitive guide: big data processing made simple | | Material Type: | printed text | | Authors: | Bill Chambers, Author ; Matei Zaharia, Author | | Edition statement: | 1st edition | | Publisher: | Sebastopol, CA : O'Reilly & Associates | | Publication Date: | 2018 | | Pagination: | xxvi, 576 p. | | Layout: | ill. | | Size: | 24 cm | | ISBN (or other code): | 978-1-491-91221-8 | | General note: | Includes index | | Languages : | English (eng) Original Language : English (eng) | | Descriptors: | Computer programs
Data mining
Electronic data processing
Spark (Electronic resource - Apache Software Foundation)
Telecommunication - Message processing.
Web applications - Development
Web servers - Computer programs
| | Class number: | 006.312 | | Abstract: | "Spark : The Definitive Guide"- Learn how tu use, deploy, and maintain Apache Spark with this comprehensive guide, written by some of the creators of this open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.O, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s 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 MLIib, Spark’s scalable machine learning library.
-Get a gentle overview of big data and Spark
-Learn about DataFrames, SQL, and Datasets-Spark core APIs-through worked examples
-Dive into Spark’s low-level APIs, RDDs, and execution od SQL and DataFarmes
-Understand how Spark runs on a cluster
-Debug, monitor, and tune Spark clusters and applications
-Learn the power of Spark’s Structured Streaming, Sparks’s stream processing engine
-Learn about MLIib and how you can apply it to a variety of problems including cllasification or recommendation | | Contents note: | Gentle overview of big data and Spark. What is Apache Spark?; A gentle introduction to Spark; A tour of Spark's toolset; 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; Low-level APIs. Resilient distributed datasets (RDDs); Advanced RDDs; Distributed shared variables; Production applications. How Spark runs on a cluster; Developing Spark applications; Deploying Spark; Monitoring and debugging; Performance tuning; Streaming. Stream processing fundamentals; Structured streaming basics; Event-time and stateful processing; Structured streaming in production; Advanced analytics and machine learning. Advanced analytics and machine learning overview; Preprocessing and feature engineering; Classification; Regression; Recommendation; Unsupervised learning; Graph analytics; Deep learning; Ecosystem. Language specifics : Python (PySpark) and R (SparkR and sparklyr); Ecosystem and community; | | Record link: | https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17938 |
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