Learning Spark Lightning Fast Big Data Analysis 1st Edition by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia ISBN 1449358624 9781449358624

Learning Spark Lightning Fast Big Data Analysis 1st Edition by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia – Ebook PDF Instant Download/Delivery. 1449358624 ,9781449358624
Full download Learning Spark Lightning Fast Big Data Analysis 1st Edition after payment

Product details:
ISBN 10: 1449358624
ISBN 13: 9781449358624
Author: Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.

• Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell
• Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib
• Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm
• Learn how to deploy interactive, batch, and streaming applications
• Connect to data sources including HDFS, Hive, JSON, and S3
• Master advanced topics like data partitioning and shared variables

Learning Spark Lightning Fast Big Data Analysis 1st Edition Table of contents:

Chapter 1: Introduction to Apache Spark

• What is Apache Spark?
• The Spark Ecosystem: Components and Use Cases
• Key Features of Spark
• Spark vs. Hadoop
• Getting Started with Spark
• Running Spark on Your Local Machine

Chapter 2: Spark Basics

• Introduction to Spark Programming
• RDDs (Resilient Distributed Datasets) and DataFrames
• Creating RDDs and DataFrames
• Transformations and Actions in Spark
• Lazy Evaluation
• Caching in Spark

Chapter 3: Spark DataFrames and SQL

• Introduction to Spark DataFrames
• Operations on DataFrames
• Using SQL with Spark
• Using the Spark SQL API
• Connecting to External Databases
• Managing DataFrames and Tables

Chapter 4: Working with Data

• Loading Data into Spark
• Reading from Various Data Sources (CSV, JSON, Parquet, etc.)
• Data Transformation and Cleansing
• Working with Structured Data
• Joining DataFrames and RDDs

Chapter 5: Spark’s Machine Learning Library (MLlib)

• Introduction to MLlib
• Classification, Regression, and Clustering
• Building Machine Learning Pipelines in Spark
• Evaluating Models in Spark
• Feature Engineering and Feature Selection
• Tuning Hyperparameters in Spark MLlib

Chapter 6: Spark Streaming

• What is Spark Streaming?
• Processing Real-Time Data with Spark Streaming
• DStreams and Transformations
• Integrating with Kafka and Other Streaming Sources
• Stateful Transformations
• Windowed Operations and Time-based Processing

Chapter 7: Spark on the Cloud

• Running Spark on Cloud Platforms (AWS, Azure, Google Cloud)
• Setting up Spark on EC2 and other Cloud Services
• Using Spark on Databricks
• Managing Spark Clusters on the Cloud
• Performance Tuning for Cloud Environments

Chapter 8: Performance Tuning and Optimization

• Understanding Spark’s Performance Characteristics
• Best Practices for Tuning Spark Jobs
• Partitioning and Shuffling Data
• Configuring Spark for Maximum Efficiency
• Monitoring and Debugging Spark Jobs

Chapter 9: Advanced Spark Features

• Spark SQL Optimization Techniques
• Broadcast Variables and Accumulators
• Custom Transformations and Actions
• Using GraphX for Graph Processing
• Working with SparkR and PySpark

Chapter 10: Case Studies and Real-World Applications

• Case Study 1: Building a Data Pipeline with Spark
• Case Study 2: Machine Learning with Spark MLlib
• Case Study 3: Real-Time Data Analysis with Spark Streaming
• Performance Tuning for Large-Scale Data Processing

Appendices

• A: Installing Spark and Setting Up the Environment
• B: Using the Spark Shell and REPL
• C: Resources for Learning More about Apache Spark
• D: Spark and Hadoop Integration

People also search for Learning Spark Lightning Fast Big Data Analysis 1st Edition:

learning spark lightning fast big data analysis pdf

borrow learning spark lightning fast big data analysis

learning spark lightning fast big data analysis matei zaharia 2015

learning spark lightning fast big data analysis 2nd edition