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Data analytics with Hadoop : an introduction for data scientists /
Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you'll focus on particular a...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2016.
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Copyright; Table of Contents; Preface; What to Expect from This Book; Who This Book Is For; How to Read This Book; Overview of Chapters; Programming and Code Examples; GitHub Repository; Executing Distributed Jobs; Permissions and Citation; Feedback and How to Contact Us; Safari® Books Online; How to Contact Us; Acknowledgments; Part I. Introduction to Distributed Computing; Chapter 1. The Age of the Data Product; What Is a Data Product?; Building Data Products at Scale with Hadoop; Leveraging Large Datasets; Hadoop for Data Products; The Data Science Pipeline and the Hadoop Ecosystem.
- Big Data WorkflowsConclusion; Chapter 2. An Operating System for Big Data; Basic Concepts; Hadoop Architecture; A Hadoop Cluster; HDFS; YARN; Working with a Distributed File System; Basic File System Operations; File Permissions in HDFS; Other HDFS Interfaces; Working with Distributed Computation; MapReduce: A Functional Programming Model; MapReduce: Implemented on a Cluster; Beyond a Map and Reduce: Job Chaining; Submitting a MapReduce Job to YARN; Conclusion; Chapter 3. A Framework for Python and Hadoop Streaming; Hadoop Streaming; Computing on CSV Data with Streaming.
- Executing Streaming JobsA Framework for MapReduce with Python; Counting Bigrams; Other Frameworks; Advanced MapReduce; Combiners; Partitioners; Job Chaining; Conclusion; Chapter 4. In-Memory Computing with Spark; Spark Basics; The Spark Stack; Resilient Distributed Datasets; Programming with RDDs; Interactive Spark Using PySpark; Writing Spark Applications; Visualizing Airline Delays with Spark; Conclusion; Chapter 5. Distributed Analysis and Patterns; Computing with Keys; Compound Keys; Keyspace Patterns; Pairs versus Stripes; Design Patterns; Summarization; Indexing; Filtering.
- Toward Last-Mile AnalyticsFitting a Model; Validating Models; Conclusion; Part II. Workflows and Tools for Big Data Science; Chapter 6. Data Mining and Warehousing; Structured Data Queries with Hive; The Hive Command-Line Interface (CLI); Hive Query Language (HQL); Data Analysis with Hive; HBase; NoSQL and Column-Oriented Databases; Real-Time Analytics with HBase; Conclusion; Chapter 7. Data Ingestion; Importing Relational Data with Sqoop; Importing from MySQL to HDFS; Importing from MySQL to Hive; Importing from MySQL to HBase; Ingesting Streaming Data with Flume; Flume Data Flows.
- Ingesting Product Impression Data with FlumeConclusion; Chapter 8. Analytics with Higher-Level APIs; Pig; Pig Latin; Data Types; Relational Operators; User-Defined Functions; Wrapping Up; Spark's Higher-Level APIs; Spark SQL; DataFrames; Conclusion; Chapter 9. Machine Learning; Scalable Machine Learning with Spark; Collaborative Filtering; Classification; Clustering; Conclusion; Chapter 10. Summary: Doing Distributed Data Science; Data Product Lifecycle; Data Lakes; Data Ingestion; Computational Data Stores; Machine Learning Lifecycle; Conclusion.