Mastering Spark with R The Complete Guide to Large Scale Analysis and Modeling 1st Edition by Javier Luraschi, Kevin Kuo, Edgar Ruiz ISBN 149204637X 978-1492046370

Mastering Spark with R The Complete Guide to Large Scale Analysis and Modeling 1st Edition by Javier Luraschi, Kevin Kuo, Edgar Ruiz  – Ebook PDF Instant Download/Delivery: 149204637X, 978-1492046370
Full download Mastering Spark with R The Complete Guide to Large Scale Analysis and Modeling 1st Edition after payment

Product details: 

ISBN 10: 149204637X

ISBN 13: 978-1492046370

Author: Javier Luraschi, Kevin Kuo, Edgar Ruiz 

If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.

Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users.

Table of contents:

1. Introduction. 

Overview 

Hadoop 

Spark 

R 

sparklyr 

Recap 

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 

3. Analysis. 

Overview 

Import 

Wrangle 

Built-in Functions. 

Correlations 

Visualize 

Using ggplot2 

Using dbplot 

Model 

Caching 

Communicate 

Recap 

4. Modeling. 

Overview 

Exploratory Data Analysis 

Feature Engineering 

Supervised Learning 

Generalized Linear Regression 

Other Models 

Unsupervised Learning 

Data Preparation 

Topic Modeling 

Recap 

5. Pipelines. 

Overview 

Creation 

Use Cases 

Hyperparameter Tuning 

Operating Modes 

Interoperability 

Deployment 

Batch Scoring 

Real-Time Scoring 

Recap

6. Clusters. 

Overview 

On-Premises 

Managers 

Distributions 

Cloud 

Amazon 

Databricks 

Google 

IBM 

Microsoft 

Qubole 

Kubernetes 

Tools 

RStudio 

Jupyter 

Livy 

Recap 

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

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 

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

10. Extensions. 

Overview 

H2O 

Graphs 

XGBoost 

Deep Learning 

Genomics 

Spatial 

Troubleshooting 

Recap 

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 

12. Streaming. 

Overview 

Transformations 

Analysis 

Modeling 

Pipelines 

Distributed R

Kafka 

Shiny 

Recap 

13. Contributing. 

Overview 

The Spark API 

Spark Extensions 

Using Scala Code 

Recap

People also search for:

mastering the mix review
spark master configuration
mastering spark sql
mastering with reason 12
mastering with span

Tags: Javier Luraschi, Kevin Kuo, Edgar Ruiz, Mastering Spark, Complete Guide, Large Scale Analysis, Modeling