Build a Vagrant Scala spark-shell cluster and Code/Monitor against Spark 2 Core. The modern cluster computation engine.

Course Access

You can access all the Big Data / Spark courses for one low monthly fee. Currently the membership site houses courses that covers deploying Hadoop with Cloudera and Hortonworks as well as installing and working with Spark 2.0.

• POC-d membership site : POC-D Membership site

This course can be purchased from

• Udemy : POC-D Udemy BigData/Spark courses

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.

Recommended Spark course path. If you already have spark installed, you do not need to access the first three courses