Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling
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.
- Analyze, explore, transform, and visualize data in Apache Spark with R
- Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows
- Perform analysis and modeling across many machines using distributed computing techniques
- Use large-scale data from multiple sources and different formats with ease from within Spark
- Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale
- Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions
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
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