Real World Spark 2 - Scala interactivo Scala shell Core

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Real World Spark 2 – Scala interactivo Scala shell Core. Crear un clúster pyspark Vagrant Código Python y / Monitor contra Spark Core 2. El motor de cálculo clúster moderna.

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No te pierdas este fabuloso curso online llamado Real World Spark 2 – Scala interactivo Scala shell Core. Es 100% online y comenzarás justo en el momento de matricularte. Tú serás el que marques tu propio ritmo de aprendizaje.

Breve descripción del curso llamado Real World Spark 2 – Scala interactivo Scala shell Core

Crear un clúster pyspark Vagrant Código Python y / Monitor contra Spark Core 2. El motor de cálculo clúster moderna.

El profesor de este fabuloso curso 100% online es Toyin Akin, un auténtico experto en la materia, y con el que aprenderás todo lo necesario para ser más competitivo. El curso se ofrece en Inglés.

Descripción completa del curso llamado Real World Spark 2 – Scala interactivo Scala shell Core

Course Description Note : This course is built on top of the “Real World Vagrant – Build an Apache Spark Development Env! – Toyin Akin” course. So if you do not have a Spark environment already installed (within a VM or directly installed), you can take the stated course above. Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in Scala (which runs on the Java VM and is thus a good way to use existing Java libraries). Start it by running the following anywhere within a bash terminal within the built Virtual Machine spark-shell Spark’s primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from collections, Hadoop InputFormats (such as HDFS files) or by transforming other RDDs Spark Monitoring and Instrumentation While creating RDDs, performing transformations and executing actions, you will be working heavily within the monitoring view of the Web UI. Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes: A list of scheduler stages and tasks A summary of RDD sizes and memory usage Environmental information. Information about the running executors Why Apache Spark … Apache Spark run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Apache Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing. Apache Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells. Apache Spark can combine SQL, streaming, and complex analytics. Apache Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.