Simple setup for it-hadoop-client

I found that by just following the instructions at https://hadoop-user-guide.web.cern.ch/hadoop-user-guide/gettingstarted_md.html I can submit this minimal job:
```python
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession

conf = SparkConf().setMaster("yarn").setAppName("CMS Working Set")
sc = SparkContext(conf=conf)
spark = SparkSession(sc)

readavro = spark.read.format("com.databricks.spark.avro")
fwjr = readavro.load("/cms/wmarchive/avro/fwjr/201[789]/*/*/*.avro")
```
with
```sh
spark-submit --packages com.databricks:spark-avro_2.11:4.0.0 test.py
```

Perhaps this is a better soft introduction than the RDD complexity? Also, there seem to be lxplus options.

Start with the linked Hadoop getting-started guide and the minimal test.py example, then check how this repository currently introduces RDD-based workflows. Verify the spark-submit command and investigate the mentioned lxplus options. Done means the project has a clear introductory setup path, with the Spark/Avro example and lxplus guidance positioned appropriately.

Written by the indexing model from the issue text.