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Learning Spark: Lightning-Fast Data Analytics

Learning Spark: Lightning-Fast Data Analytics

Learning Spark: Lightning-Fast Data Analytics
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Learning Spark: Lightning-Fast Data Analytics Paperback - 2020

by Damji, Jules S

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Used - Good. Item in good condition. Textbooks may not include supplemental item i.e. CDs, access codes, etc.. May have some notes or highlighting, etc.. Item in good condition. Textbooks may not include supplemental item i.e. CDs, access codes, etc.. May have some notes or highlighting, etc..
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  • Title Learning Spark: Lightning-Fast Data Analytics
  • Author Damji, Jules S
  • Binding Paperback
  • Condition Used - Good
  • Pages 397
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2020-08-25
  • Illustrated Yes
  • Features Illustrated, Index
  • Bookseller's Inventory # 1492050040-G
  • ISBN 9781492050049 / 1492050040
  • Weight 1.4 lbs (0.64 kg)
  • Dimensions 9.2 x 7 x 0.9 in (23.37 x 17.78 x 2.29 cm)
  • Category Computers - General Information
  • Library of Congress subjects Machine learning, Data mining - Computer programs
  • Dewey Decimal Code 006.312
  • Quantity available 1

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From the publisher

Data is bigger, arrives faster, and comes in a variety of formats and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark.

Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you ll be able to:

  • Learn Python, SQL, Scala, or Java high-level Structured APIs
  • Understand Spark operations and SQL Engine
  • Inspect, tune, and debug Spark operations with Spark configurations and Spark UI
  • Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka
  • Perform analytics on batch and streaming data using Structured Streaming
  • Build reliable data pipelines with open source Delta Lake and Spark
  • Develop machine learning pipelines with MLlib and productionize models using MLflow

About the author

Jules S. Damji is a senior developer advocate at Databricks and an MLflow contributor. He is a hands-on developer with over 20 years of experience and has worked as a software engineer at leading companies such as Sun Microsystems, Netscape, @Home, Loudcloud/Opsware, Verisign, ProQuest, and Hortonworks, building large scale distributed systems. He holds a B.Sc. and an M.Sc. in computer science and an MA in political advocacy and communication from Oregon State University, Cal State, and Johns Hopkins University, respectively.

Brooke Wenig is a machine learning practice lead at Databricks. She leads a team of data scientists who develop large-scale machine learning pipelines for customers, as well as teaching courses on distributed machine learning best practices. Previously, she was a principal data science consultant at Databricks. She holds an M.S. in computer science from UCLA with a focus on distributed machine learning.

Tathagata Das is a staff software engineer at Databricks, an Apache Spark committer, and a member of the Apache Spark Project Management Committee (PMC). He is one of the original developers of Apache Spark, the lead developer of Spark Streaming (DStreams), and is currently one of the core developers of Structured Streaming and Delta Lake. Tathagata holds an M.S. in computer science from UC Berkeley.

Denny Lee is a staff developer advocate at Databricks who has been working with Apache Spark since 0.6. He is a hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premises and cloud environments. He also has an M.S. in biomedical informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise healthcare customers.

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