Information systems

1. Práctica 1

A travel agency needs an efficient management system to manage its wide range of destinations, tourist packages and customer reservations. It is requested to design and implement a travel agency management system using a NoSQL database with both the Cassandra model and the MongoDB model. The system must be able to perform basic, advanced and complex queries efficiently, which involves the generation of indexes or tables with different indexing.

Summary

In this project, I developed a travel agency management system wusing both Cassandra and MongoDB as NoSQL databases. The system allows for efficient management of destinations, tourist packages, customers, and reservations. Various types of queries, including basic, advanced, and complex, were implemented, and appropriate indexing strategies were employed to optimize query performance.

As an addition, I implemented a battery of tests to ensure consistent outputs between Cassandra and MongoDB databases.

Output example

2. Práctica 2

2.1 Income by year

Year	Income
19	34483365.68
20	8670.29

💡 2019 earnings were 34483365.68$.

2.2 Income by date (month of the year)

Date	Income
01/19	1813586.44
01/20	8670.29
02/19	2202022.42
03/19	2807100.38
04/19	3390670.24
05/19	3152606.75
06/19	2577802.26
07/19	2647775.76
08/19	2244467.88
09/19	2097560.13
10/19	3736726.88
11/19	3199603.2
12/19	4613443.34

💡 The best month for sales was December. That month the earnings were 4613443.34$.

2.3 Sales by city

City	Sales
Atlanta, GA	16602
Austin, TX	11153
Boston, MA	22528
Dallas, TX	16730
Los Angeles, CA	33289
New York City, NY	27932
Portland, ME	2750
Portland, OR	11303
San Francisco, CA	50239
Seattle, WA	16553

💡 The city with the highest number of sales in 2019 was San Francisco, CA, with a total of 50239 sales.

2.4 Sales by time of the day

Time	Sales
00	4428
01	2619
02	1398
03	928
04	937
05	1493
06	2810
07	4556
08	7002
09	9816
10	12308
11	14005
12	14202
13	13685
14	12362
15	11391
16	11662
17	12229
18	13802
19	14470
20	13768
21	12244
22	9899
23	7065

💡 We must show advertising at 7:00 p.m. to maximize the number of sales of a product, since many people are shopping online at that time.

2.5 Sales by product

Product	Sales
20in Monitor	4129
27in 4K Gaming Monitor	6244
27in FHD Monitor	7550
34in Ultrawide Monitor	6199
AA Batteries (4-pack)	27635
AAA Batteries (4-pack)	31017
Apple Airpods Headphones	15661
Bose SoundSport Headphones	13457
Flatscreen TV	4819
Google Phone	5532
LG Dryer	646
LG Washing Machine	666
Lightning Charging Cable	23217
Macbook Pro Laptop	4728
ThinkPad Laptop	4130
USB-C Charging Cable	23975
Vareebadd Phone	2068
Wired Headphones	20557
iPhone	6849

💡 The best selling product in 2019 was "AAA Batteries (4-Pack)”. I think it is because a lot of products still use AAA Batteries, like the Xbox controller.

Script used to run the Hadoop job

#!/bin/bash

jar_file="sinf.jar"
input_dir="/input/practica2"
output_dir="/output_pablo"
output_base="/output_exercise"

# Run Hadoop job
hadoop jar $jar_file $input_dir $output_dir

# Function to print and get output for each exercise
print_and_get_output() {
  exercise=$1
  output_file="./exercise$exercise.txt"
  output_path="$output_dir$output_base$exercise/par*"
  
  echo "Output exercise $exercise:"
  hadoop fs -cat $output_path

  # Get the key with the highest value
  highest_key=$(hadoop fs -cat $output_path | sort -k2,2nr -t$'\t' | head -n 1 | cut -f1)
  echo "Key with the highest value: $highest_key"

  hadoop fs -get $output_path $output_file
}

# Loop through exercises 1 to 5
for exercise in {1..5}
do
  print_and_get_output $exercise
done

# Clean up
hadoop fs -rm -r $output_dir
rm -rf ./*.txt

kyantti/sinf