Spark: The Definitive Guide: Big data processing made simple
| Author | : | |
| Rating | : | 4.59 (735 Votes) |
| Asin | : | 1491912219 |
| Format Type | : | paperback |
| Number of Pages | : | 450 Pages |
| Publish Date | : | 2016-04-14 |
| Language | : | English |
DESCRIPTION:
He has a Master's degree in Information Systems from the UC Berkeley School of Information, where he focused on data science.. Bill Chambers is a Product Manager at Databricks focusing on large-scale analytics, strong documentation, and collaboration across the organization to help customers succeed with Spark and Databricks
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 SparkLearn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examplesDive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFramesUnderstand how Spark runs on a clusterDebug, monitor, and tune Spark clusters and applicationsLearn the power of Spark’s Structured Streaming and MLlib for machine learning tasksExplore the wider Spark ecosystem, including SparkR and Graph AnalysisExamine Spark deployment, including coverage of Spark in the Cloud. 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. Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this open-source cluster-computing framework
About the AuthorBill Chambers is a Product Manager at Databricks focusing on large-scale analytics, strong documentation, and collaboration across the organization to help customers succeed with Spark and Databricks. He has a Master's degree in Information Systems from the UC Berkeley School of Information, where he focused on data science.