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Beginning Apache Spark 3 Hien Luu

Beginning Apache Spark 3 By Hien Luu

Beginning Apache Spark 3 by Hien Luu

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Summary

Beginning

Beginning Apache Spark 3 Summary

Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library by Hien Luu

Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications.

Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section.

After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications.

What You Will Learn

  • Master the Spark unified data analytics engine and its various components
  • Work in tandem to provide a scalable, fault tolerant and performant data processing engine
  • Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL
  • Develop machine learning applications using Spark MLlib
  • Manage the machine learning development lifecycle using MLflow

Who This Book Is For

Data scientists, data engineers and software developers.

About Hien Luu

Hien Luu has extensive experience in designing and building big data applications and machine learning infrastructure. He is particularly passionate about the intersection between big data and machine learning. Hien enjoys working with open source software and has contributed to Apache Pig and Azkaban. Teaching is also one of his passions, and he serves as an instructor at the UCSC Silicon Valley Extension school teaching Apache Spark. He has given presentations at various conferences such as Data+AI Summit, MLOps World, QCon SF, QCon London, Hadoop Summit, and JavaOne.

Table of Contents

Chapter 1: Introduction to Apache Spark
Chapter Goal: Provide an overview of Apache SparkNo of pages 15Sub -Topics1. Overview & history2. Spark concepts & architecture3. Spark Unified Stack4. Apache Spark applications
Chapter 2: Working with Apache SparkChapter Goal: Provide details about different ways of interacting with Apache SparkNo of pages: 35Sub - Topics 1. Downloading and Installing Apache Spark2. Exploring Apache Spark using Spark shells3. Exploring Apache Spark using Databricks4. Exploring Apache Spark source code
Chapter 3: Spark SQL - FoundationChapter Goal: Provide an overview to Spark SQL componentNo of pages: 60Sub - Topics 1. Overview & architecture2. Introduction to DataFrames Structured APIs3. Reading & writing data with Spark SQL data sources4. Introduction to datasets
Chapter 4: Spark SQL - AdvanceChapter Goal: Go over the advanced features in Spark SQLNo of pages : 50Sub - Topics: 1. Working with aggregations2. Joining data 3. Working with analytics functions4. Explore Spark SQL catalyst optimizer
Chapter 5: Optimizing Apache Spark ApplicationsChapter Goal: Go over tips and techniques for dealing with performance issues No of pages: 30Sub - Topics: 1. Common performance issues2. Speed up performance by leveraging in-memory computation3. Understand the different support joins in Spark4. Leverage Spark UI to diagnose performance issue
Chapter 6: Structured Streaming - FoundationChapter Goal: Overview of Structured Streaming processing engineNo of pages: 50Sub - Topics: 1. General streaming processing concepts2. Structured Streaming programming model3. Working with streaming data sources and sinks4. Understanding output modes and triggers
Chapter 7: Structured Streaming - AdvancedChapter Goal: Cover complex issues in streaming processingNo of pages: 40Sub - Topics: 1. Streaming processing with event time2. Stateful streaming processing3. Handling duplicate data4. Monitoring streaming processing applications
Chapter 8: Machine Learning with Apache SparkChapter Goal: How to developing Machine Learning applications using Spark MLlibNo of pages: 60Sub - Topics: 1. Machine learning overview2. Taking a tour of supported machine learning algorithms3. Building machine learning pipelines4. Machine learning tasks in action5. Parameters tuning
Chapter 9: Machine Learning Application Development w/ MLflowChapter Goal: Using MLflow to manage the Machine Learning development lifecycle No of pages: 25Sub - Topics: 1. Overview of MLflow2. Tracking machine learning development experiments3. Managing & deploying machine learning models4. Leveraging Spark for batch modeling predictions

Additional information

NLS9781484273821
9781484273821
1484273826
Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library by Hien Luu
Hien Luu
New
Paperback
APress
2021-10-23
438
N/A
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