Spark : the definitive guide : big data processing made simple / Bill Chambers and Matei Zaharia.

Format:

Book

Author/Creator:

Chambers, Bill, author.

Zaharia, Matei, author.

Language:

English

Subjects (All):

Spark (Electronic resource : Apache Software Foundation).

Information retrieval.

Data mining.

Physical Description:

1 online resource (603 pages) : illustrations

Edition:

First edition.

Place of Publication:

Sebastopol, CA : O'Reilly, February 2018.

System Details:

text file

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

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

Developing 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.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed March 5, 2018).

ISBN:

9781491912294

1491912294

9781491912201

1491912200

9781491912300

1491912308

9781491912218

1491912219

OCLC:

988029368