This post will guide you through installation of Apache Spark 2.3.

1. Download the latest version of Apache Spark to your local from here. This will download spark-x.x.x-bin-hadoop2.7.tgz.
2. Un-compress the the .tgz to your desired directory. For the purpose of this post, I will unzip it to /Users/pavanpkulkarni/Documents/spark

3. Add the below entries to your ~/.bash_profile

   #Spark Home
   export SPARK_HOME=/Users/pavanpkulkarni/Documents/spark/spark-2.3.0-bin-hadoop2.7
   export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin
   

4. Source the ~/.bash_profile file to reflect the changes.

   source ~/.bash_profile
   

5. Verify installation

   Pavans-MacBook-Pro:~ pavanpkulkarni$ spark-shell
   2018-04-09 14:00:15 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
   Setting default log level to "WARN".
   To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
   Spark context Web UI available at http://10.0.0.67:4040
   Spark context available as 'sc' (master = local[*], app id = local-1523296821403).
   Spark session available as 'spark'.
   Welcome to
         ____              __
        / __/__  ___ _____/ /__
       _\ \/ _ \/ _ `/ __/  '_/
      /___/ .__/\_,_/_/ /_/\_\   version 2.3.0
         /_/
   
   Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_152)
   Type in expressions to have them evaluated.
   Type :help for more information.
   
   scala> 
   

   Web UI should be available at - http://localhost:4040/

6. Run a sample code in spark-shell

   scala> val rdd = sc.parallelize(1 to 1000000, 10)
   rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24
   
   scala> rdd.count()
   res0: Long = 1000000
   
   scala> rdd.take(20)
   res1: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
   
   scala> val rdd1 = rdd.map( _ + 1 )
   rdd1: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[1] at map at <console>:25
   
   scala> rdd1.count()
   res2: Long = 1000000
   
   scala> rdd1.take(20)
   res3: Array[Int] = Array(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)