maneeshdisodia

from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.functions import lit
import logging

# Initialize Spark session
spark = SparkSession.builder.appName("ConcatDataFrames").getOrCreate()

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("ConcatLogger")

maneeshdisodia

= Why JIRA should use Neo4j

== Introduction

There are few developers in the world that have never used an issue tracker. But there are even fewer developers who have ever used an issue tracker which uses a graph database. This is a shame because issue tracking really maps much better onto a graph database, than it does onto a relational database. Proof of that is the https://developer.atlassian.com/download/attachments/4227160/JIRA61_db_schema.pdf?api=v2[JIRA database schema].

Now obviously, the example below does not have all of the features that a tool like JIRA provides. But it is only a proof of concept, you could map every feature of JIRA into a Neo4J database. What I've done below, is take out some of the core functionalities and implement those. 

== The data set