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Apache Spark ™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. Simple. Fast. Scalable. Unified. Key features. Batch/streaming data. Unify the processing of your data in batches and real-time streaming, using your preferred language: Python, SQL, Scala, Java or R.
Apache Spark (Spark) es un motor de procesamiento de datos de código abierto para grandes conjuntos de datos. Está diseñado para ofrecer la velocidad computacional, la escalabilidad y la capacidad de programación necesarias para Big Data, específicamente para la transmisión de datos, datos de gráficos, machine learning y aplicaciones de ...
7 de may. de 2020 · Descúbrela en detalle: sus funciones y ventajas a la hora de trabajar con grandes volúmenes de datos. Apache Spark es un sistema de computación distribuido de código abierto basado en Hadoop, pensado para el análisis y procesamiento de datos en los campos del Big Data y el Machine Learning.
Apache Spark - A Unified engine for large-scale data analytics. Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
Apache Spark is a fast and flexible framework for processing large-scale data with built-in modules for SQL, streaming, machine learning, and graph processing. Learn how Spark differs from Hadoop, what are its benefits and features, and how to use it on Google Cloud with Dataproc.
Caching. Self-Contained Applications. Where to Go from Here. This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python.
Apache Spark™ Documentation. Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: Spark 3.5.1. Spark 3.5.0. Spark 3.4.3. Spark 3.4.2. Spark 3.4.1. Spark 3.4.0. Spark 3.3.4. Spark 3.3.3. Spark 3.3.2. Spark 3.3.1. Spark 3.3.0. Spark 3.2.4. Spark 3.2.3. Spark 3.2.2. Spark 3.2.1.
