دانلود کتاب Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling

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Cover
Copyright
Table of Contents
Foreword
Preface
Formatting
Acknowledgments
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Chapter 1. Introduction
Overview
Hadoop
Spark
R
sparklyr
Recap
Chapter 2. Getting Started
Overview
Prerequisites
Installing sparklyr
Installing Spark
Connecting
Using Spark
Web Interface
Analysis
Modeling
Data
Extensions
Distributed R
Streaming
Logs
Disconnecting
Using RStudio
Resources
Recap
Chapter 3. Analysis
Overview
Import
Wrangle
Built-in Functions
Correlations
Visualize
Using ggplot2
Using dbplot
Model
Caching
Communicate
Recap
Chapter 4. Modeling
Overview
Exploratory Data Analysis
Feature Engineering
Supervised Learning
Generalized Linear Regression
Other Models
Unsupervised Learning
Data Preparation
Topic Modeling
Recap
Chapter 5. Pipelines
Overview
Creation
Use Cases
Hyperparameter Tuning
Operating Modes
Interoperability
Deployment
Batch Scoring
Real-Time Scoring
Recap
Chapter 6. Clusters
Overview
On-Premises
Managers
Distributions
Cloud
Amazon
Databricks
Google
IBM
Microsoft
Qubole
Kubernetes
Tools
RStudio
Jupyter
Livy
Recap
Chapter 7. Connections
Overview
Edge Nodes
Spark Home
Local
Standalone
YARN
YARN Client
YARN Cluster
Livy
Mesos
Kubernetes
Cloud
Batches
Tools
Multiple Connections
Troubleshooting
Logging
Spark Submit
Windows
Recap
Chapter 8. Data
Overview
Reading Data
Paths
Schema
Memory
Columns
Writing Data
Copying Data
File Formats
CSV
JSON
Parquet
Others
File Systems
Storage Systems
Hive
Cassandra
JDBC
Recap
Chapter 9. Tuning
Overview
Graph
Timeline
Configuring
Connect Settings
Submit Settings
Runtime Settings
sparklyr Settings
Partitioning
Implicit Partitions
Explicit Partitions
Caching
Checkpointing
Memory
Shuffling
Serialization
Configuration Files
Recap
Chapter 10. Extensions
Overview
H2O
Graphs
XGBoost
Deep Learning
Genomics
Spatial
Troubleshooting
Recap
Chapter 11. Distributed R
Overview
Use Cases
Custom Parsers
Partitioned Modeling
Grid Search
Web APIs
Simulations
Partitions
Grouping
Columns
Context
Functions
Packages
Cluster Requirements
Installing R
Apache Arrow
Troubleshooting
Worker Logs
Resolving Timeouts
Inspecting Partitions
Debugging Workers
Recap
Chapter 12. Streaming
Overview
Transformations
Analysis
Modeling
Pipelines
Distributed R
Kafka
Shiny
Recap
Chapter 13. Contributing
Overview
The Spark API
Spark Extensions
Using Scala Code
Recap
Appendix A. Supplemental Code References
Preface
Formatting
Chapter 1
The World’s Capacity to Store Information
Daily Downloads of CRAN Packages
Chapter 2
Prerequisites
Chapter 3
Hive Functions
Chapter 4
MLlib Functions
Chapter 6
Google Trends for On-Premises (Mainframes), Cloud Computing, and Kubernetes
Chapter 12
Stream Generator
Installing Kafka
Index
About the Authors
Colophon