> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/spark-home.md).

# Spark Home

### spark home

Now you are in SPARK\_HOME

In my example, SPARK\_HOME=/home/bigdata2/spark/spark

Append following in \~/.bashrc

export SPARK\_HOME=/home/bigdata2/spark/spark

export PATH=$SPARK\_HOME/bin:$PATH

Log out and log back in.

Now you are ready for works on sparks that will integrate with Hadoop and Hive

Before start spark master service, set following environment manually on command line

export LD\_LIBRARY\_PATH=$HADOOP\_HOME/lib/native

To start SPARK cluster, run:

$SPARK\_HOME/sbin/start-all.sh

Once spark cluster that has master and worker nodes (in our cluster, Spark master and worker nodes are on the same machine. You can see spark cluster information by connect to the server at port 8080

![](/files/-M1dwIE5dvdF_wXlKryC)

Now the environment is ready for you to start develop spark code on your development workstation and deploy your code to the spark cluster that will run it.

Working on Apache Spark means lots of coding with APIs provided by Apache Spark libraries that include SQL, Machine Learning, Streaming and Graph computing.

Spark supports Scala, Python, Java and R.

In our class, we will only focus on Scala and Python for all hands-on programming.


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