| 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. 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 |
| Summary |
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the 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. 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 Structured Streaming, Spark's stream-processing engine. Learn how you can apply MLlib to a variety of problems, including classification or recommendation.--Provided by publisher |
| Notes |
Includes index |
|
Online resource; title from title page (Safari, viewed May 22, 2017) |
| SUBJECT |
Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445
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Spark (Electronic resource : Apache Software Foundation) fast |
| Subject |
Data mining.
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Information retrieval.
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Big data.
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information retrieval.
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COMPUTERS -- Computer Literacy.
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COMPUTERS -- Computer Science.
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COMPUTERS -- Data Processing.
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COMPUTERS -- Hardware -- General.
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COMPUTERS -- Information Technology.
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COMPUTERS -- Machine Theory.
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COMPUTERS -- Reference.
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Big data
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Data mining
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Information retrieval
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| Form |
Electronic book
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| Author |
Zaharia, Matei, author.
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| LC no. |
2017278927 |
| ISBN |
9781491912300 |
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1491912308 |
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9781491912294 |
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1491912294 |
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9781491912201 |
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1491912200 |
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1491912219 |
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9781491912218 |
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