Load Tables

//Clear Existing nodes and relationship from database match (a) optional match (a)-[r]-() delete a,r;

//LOAD airlines table LOAD CSV WITH HEADERS FROM “file:///airlines.csv” AS airlines CREATE (al: Airline {car_id: airlines.carrier, car_name: airlines.name});

//LOAD airports table LOAD CSV WITH HEADERS FROM “file:///airports.csv” AS airports CREATE (ap: Airport {ap_id: airports.faa, ap_name: airports.name, lat:airports.lat, lon:airports.lon, alt: airports.alt, tz: airports.tz, dst: airports.dst});

//LOAD planes table LOAD CSV WITH HEADERS FROM “file:///planes.csv” AS planes CREATE(p: Plane {tailnum: planes.tailnum, prod_yr: planes.year, p_type: planes.type, mfr: planes.manufacturer, model: planes.model, num_engines: planes.engines, seats: planes.seats, speed:planes.speed, engine_typ: planes.engine});

//LOAD flights table LOAD CSV WITH HEADERS FROM “file:///flights.csv” AS flights CREATE(f: Flight {year: flights.year, month: flights.month, day: flights.day, dep_time: flights.dep_time, arr_time: flights.arr_time, carrier: flights.carrier, flight_num: flights.flight, air_time: flights.air_time, distance: flights.distance, hour: flights.hour, minute: flights.minute});

//LOAD weather table LOAD CSV WITH HEADERS FROM “file:///weather.csv” AS wth CREATE(w: Weather {year:wth.year, month: wth.month, day:wth.day, hour:wth.hour, origin:wth.origin, temp:wth.temp, dewp:wth.dewp, humid:wth.humid, wind_dir:wth.wind_dir, wind_speed:wth.wind_speed, wind_gust:wth.wind_gust, precip:wth.precip, pressure: wth.pressure, visib:wth.visib});

Define Relationships

//Relate Flight to Airline Carrier LOAD CSV WITH HEADERS FROM “file:///flights.csv” AS flights MATCH (a:Flight{carrier:flights.carrier}) MATCH (b:Airline{car_id:flights.carrier}) MERGE (a)-[r:IS_CARRIER]->(b);

//Relate Flight to plane info LOAD CSV WITH HEADERS FROM “file:///flights.csv” AS flights MATCH (a:Flight{flight_num:flights.flight, year:flights.year, month:flights.month, day:flights.day, dep_time:flights.dep_time, carrier:flights.carrier}) MATCH (b:Plane{tailnum:flights.tailnum}) MERGE (a)-[r:HAS_PLANE_INFO]->(b)

//Relate Flight to arrival airport LOAD CSV WITH HEADERS FROM “file:///flights.csv” AS flights MATCH (a:Flight{flight_num:flights.flight, year:flights.year, month:flights.month, day:flights.day, dep_time:flights.dep_time, carrier:flights.carrier}) MATCH (b:Airport{ap_id:flights.dest}) MERGE (a)-[r:ARRIVES{arr_delay:flights.arr_delay}]->(b)

//Relate Flight to destination LOAD CSV WITH HEADERS FROM “file:///flights.csv” AS flights MATCH (a:Flight{flight_num:flights.flight, year:flights.year, month:flights.month, day:flights.day, dep_time:flights.dep_time, carrier:flights.carrier}) MATCH (b:Airport{ap_id:flights.origin}) MERGE (a)-[r:LEAVES]->(b)

//Relate flight departure to weather conditons LOAD CSV WITH HEADERS FROM “file:///flights.csv” AS flights MATCH (a:Flight{flight_num:flights.flight, year:flights.year, month:flights.month, day:flights.day, dep_time:flights.dep_time, carrier:flights.carrier}) MATCH (b: Weather{year: flights.year, month: flights.month, day: flights.day, hour: flights.hour, origin:flights.origin}) MERGE (a)-[r:HAS_WEATHER]->(b)

Pros

1.NOSQL queries often execute much faster than those used in relational models, particularly compared to SQL databases with 20+ table joins.

2.Data summaries and basic manipulations can often be achieved using significantly less code; so code readability is improved.

3.NOSQL environments are not restricted to the rigid schema definitions imposed by typical RDMS models.

Cons

1.Most software developers are comfortable working with SQL-based databases; however, there are far fewer individuals accustomed to working in NOSQL environments. This can be a problem for employers with immediate business needs.

2.rigidity imposed by RDMS models is often seen as a positive for employers with transactional processes that require near-perfect accuracy. SQL-based systems are generally perceived as being superior to NOSQL models in this regard.