Reviews Author: Bill Chambers, Matei Zaharia Pub Date: 2017 ISBN: 9218 Pages: 450 Language: English Format: PDF (Early Release) Size: 10 Mb Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this 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. 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 MLlib, Spark’s scalable machine learning library. Bigdata / Spark - The Definitive Guide - Big data processing made simple.pdf Find file Copy path achinnasamy Add files via upload c707bc2 May 10, 2018. • Get a gentle overview of big data and Spark • Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples • Dive into Spark’s 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 Spark’s Structured Streaming and MLlib for machine learning tasks • Explore the wider Spark ecosystem, including SparkR and Graph Analysis • Examine Spark deployment, including coverage of Spark in the Cloud. ![]() Find more information about: ISBN: 33994919912200 OCLC Number: 988029368 Notes: Includes index. Description: 1 online resource (xxvi, 576 pages): illustrations 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. Watch saint seiya english dubbed online. Production applications. How Spark runs on a cluster -- Developint Spark applications -- Deploying Spark -- Monitoring and debugging -- Performance tuning -- Part 5. Calvin harris alesso under control. Calvin Harris & Alesso - Under Control ft. ![]() 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. Language specifics: Python (PySpark) and R (SparkR and sparklyr) -- Ecosystem and community. Responsibility: Bill Chambers and Matei Zaharia.
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