Spark : the definitive guide : big data processing made simple /

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sec...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Chambers, Bill (William Andrew) (Autor), Zaharia, Matei (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, [2018]
Edición:First edition.
Temas:
Spark (Electronic resource : Apache Software Foundation)
Data mining.
Information retrieval.
Big data.
Exploration de données (Informatique)
Recherche de l'information.
Données volumineuses.
information retrieval.
COMPUTERS > Computer Literacy.
COMPUTERS > Computer Science.
COMPUTERS > Data Processing.
COMPUTERS > Hardware > General.
COMPUTERS > Information Technology.
COMPUTERS > Machine Theory.
COMPUTERS > Reference.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Part 1. Gentle overview of big data and Spark. What is Apache Spark?
  • A gentle introduction to Spark
  • A tour of Spark's toolset
  • Part 2. Structured APIs : DataFrames, SQL, and datasets. Structured API overview
  • Basic structured operations
  • Working with different types of data
  • Aggregations
  • Joins
  • Data sources
  • Spark SQL
  • Datasets
  • Part 3. Low-level APIs. Resilient distributed datasets (RDDs)
  • Advanced RDDs
  • Distributed shared variables
  • Part 4. Production applications. How Spark runs on a cluster
  • Developint Spark applications
  • Deploying Spark
  • Monitoring and debugging
  • Performance tuning
  • Part 5. Streaming. Stream processing fundamentals
  • Structured streaming basics
  • Event-time and stateful processing
  • Structured streaming in production
  • Part 6. Advanced analytics and machine learning. Advanced analytics and machine learning overview
  • Preprocessing and feature engineering
  • Classification
  • Regression
  • Recommendation
  • Unsupervised learning
  • Graph analytics
  • Deep learning
  • Part 7. Ecosystem. Language specifics : Python (PySpark) and R (SparkR and sparklyr)
  • Ecosystem and community.

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