PROJECT 4 - NoSQL migration
Advantages
NoSQL
Database can handle Large volumes of structured, semi-structured, and unstructured data.
The database is structure for Speed.
No SQL databases can be scaled by adding non commonly used servers.
Dynamic Schema ensures the flexibility to add new fields for data.
SQL
SQL databases have a predefine Schema.
Each table is structured and defined.
Acid compliant (Atomicity, Consistency, Isolation, Durability)
Reliable support by vendor.
Disadvantages
NOSQL
NO SQL databases have been around for a short period of time.
Support can be limited to the NO SQL community.
Not Acid compliant.
SQL
Vendor pricing can be very high.
Database may be compatible to specific hardware
Limited compatibility such as running on windows based servers instead of UNIX.
Migration
I first start by connecting to MySQL database in my local computer. The database i will be migrating is the Flights database given at the beginning of the course.
Flight Schema
After connecting to the local database I created a separate query for each table. Using Tidyr I modified the dates columns in the flightsdf and weatherdf data frame to a standard date format. Packages used in the below step are RMYSQL and Tidyr.
# set MySQL information
Yuser = 'root'
Ydbname ='flights'
Yhost = 'localhost'
# create db connection with password promt
mydb = dbConnect(MySQL(), user= Yuser, password=getPass('Enter Database Password'), dbname=Ydbname, host=Yhost)
# query for the view
planesq = dbSendQuery(mydb,"select * from planes;")
# place query values in data frame
planesdf = data.frame( fetch(planesq, n=-1))
head(planesdf,5) tailnum year type manufacturer model engines
1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
2 N102UW 1998 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
3 N103US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
4 N104UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
5 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR 2
seats speed engine
1 55 NA Turbo-fan
2 182 NA Turbo-fan
3 182 NA Turbo-fan
4 182 NA Turbo-fan
5 55 NA Turbo-fanflightsq = dbSendQuery(mydb,"select * from flights;")
# place query values in data frame
flightsdf = data.frame( fetch(flightsq, n=-1))
head(flightsdf,5) year month day dep_time dep_delay arr_time arr_delay carrier tailnum
1 2013 1 1 517 2 830 11 UA N14228
2 2013 1 1 533 4 850 20 UA N24211
3 2013 1 1 542 2 923 33 AA N619AA
4 2013 1 1 544 -1 1004 -18 B6 N804JB
5 2013 1 1 554 -6 812 -25 DL N668DN
flight origin dest air_time distance hour minute
1 1545 EWR IAH 227 1400 5 17
2 1714 LGA IAH 227 1416 5 33
3 1141 JFK MIA 160 1089 5 42
4 725 JFK BQN 183 1576 5 44
5 461 LGA ATL 116 762 6 54airportsq = dbSendQuery(mydb,"select * from airports;")
# place query values in data frame
airportsdf = data.frame( fetch(airportsq, n=-1))
head(airportsdf,5) faa name lat lon alt tz dst
1 04G Lansdowne Airport 41.13047 -80.61958 1044 -5 A
2 06A Moton Field Municipal Airport 32.46057 -85.68003 264 -5 A
3 06C Schaumburg Regional 41.98934 -88.10124 801 -6 A
4 06N Randall Airport 41.43191 -74.39156 523 -5 A
5 09J Jekyll Island Airport 31.07447 -81.42778 11 -4 Aweatherq = dbSendQuery(mydb,"select * from weather;")
# place query values in data frame
weatherdf = data.frame( fetch(weatherq, n=-1))
head(weatherdf,5) origin year month day hour temp dewp humid wind_dir wind_speed
1 EWR 2013 1 1 0 37.04 21.92 53.97 230 10.35702
2 EWR 2013 1 1 1 37.04 21.92 53.97 230 13.80936
3 EWR 2013 1 1 2 37.94 21.92 52.09 230 12.65858
4 EWR 2013 1 1 3 37.94 23.00 54.51 230 13.80936
5 EWR 2013 1 1 4 37.94 24.08 57.04 240 14.96014
wind_gust precip pressure visib
1 11.91865 0 1013.9 10
2 15.89154 0 1013.0 10
3 14.56724 0 1012.6 10
4 15.89154 0 1012.7 10
5 17.21583 0 1012.8 10airlinesq = dbSendQuery(mydb,"select * from airlines;")
# place query values in data frame
airlinesdf = data.frame( fetch(airlinesq, n=-1))
head(airlinesdf,5) carrier name
1 9E Endeavor Air Inc.\r
2 AA American Airlines Inc.\r
3 AS Alaska Airlines Inc.\r
4 B6 JetBlue Airways\r
5 DL Delta Air Lines Inc.\r# close database connection
dbDisconnect(mydb)Warning: Closing open result sets[1] TRUEflightdfF = unite( flightsdf, "date", month,day,year, sep = "/")
head(flightdfF,5) date dep_time dep_delay arr_time arr_delay carrier tailnum flight
1 1/1/2013 517 2 830 11 UA N14228 1545
2 1/1/2013 533 4 850 20 UA N24211 1714
3 1/1/2013 542 2 923 33 AA N619AA 1141
4 1/1/2013 544 -1 1004 -18 B6 N804JB 725
5 1/1/2013 554 -6 812 -25 DL N668DN 461
origin dest air_time distance hour minute
1 EWR IAH 227 1400 5 17
2 LGA IAH 227 1416 5 33
3 JFK MIA 160 1089 5 42
4 JFK BQN 183 1576 5 44
5 LGA ATL 116 762 6 54weatherdfF = unite(weatherdf, "date", month,day,year, sep = "/")
head(weatherdfF,5) origin date hour temp dewp humid wind_dir wind_speed wind_gust
1 EWR 1/1/2013 0 37.04 21.92 53.97 230 10.35702 11.91865
2 EWR 1/1/2013 1 37.04 21.92 53.97 230 13.80936 15.89154
3 EWR 1/1/2013 2 37.94 21.92 52.09 230 12.65858 14.56724
4 EWR 1/1/2013 3 37.94 23.00 54.51 230 13.80936 15.89154
5 EWR 1/1/2013 4 37.94 24.08 57.04 240 14.96014 17.21583
precip pressure visib
1 0 1013.9 10
2 0 1013.0 10
3 0 1012.6 10
4 0 1012.7 10
5 0 1012.8 10Mongodb
After modifying the data I created a separate connection for each data frame to be inserted into a collection/table.The code below will always drop the collection upon execution, insert the corresponding data frame, count the documents/rows, and display to documents. 
Weather
weather = mongo(collection = "Weather" ,db = "flights", url = "mongodb://localhost")
weather$drop()[1] TRUEweather$insert(weatherdfF)
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Complete! Processed total of 8719 rows.$nInserted
[1] 8719
$nMatched
[1] 0
$nRemoved
[1] 0
$nUpserted
[1] 0
$writeErrors
list()weather$count()[1] 8719weather$find(limit = 10)
Found 10 records...
Imported 10 records. Simplifying into dataframe... origin date hour temp dewp humid wind_dir wind_speed wind_gust
1 EWR 1/1/2013 0 37.04 21.92 53.97 230 10.35702 11.918651
2 EWR 1/1/2013 1 37.04 21.92 53.97 230 13.80936 15.891535
3 EWR 1/1/2013 2 37.94 21.92 52.09 230 12.65858 14.567241
4 EWR 1/1/2013 3 37.94 23.00 54.51 230 13.80936 15.891535
5 EWR 1/1/2013 4 37.94 24.08 57.04 240 14.96014 17.215830
6 EWR 1/1/2013 6 39.02 26.06 59.37 270 10.35702 11.918651
7 EWR 1/1/2013 7 39.02 26.96 61.63 250 8.05546 9.270062
8 EWR 1/1/2013 8 39.02 28.04 64.43 240 11.50780 13.242946
9 EWR 1/1/2013 9 39.92 28.04 62.21 250 12.65858 14.567241
10 EWR 1/1/2013 10 39.02 28.04 64.43 260 12.65858 14.567241
precip pressure visib
1 0 1013.9 10
2 0 1013.0 10
3 0 1012.6 10
4 0 1012.7 10
5 0 1012.8 10
6 0 1012.0 10
7 0 1012.3 10
8 0 1012.5 10
9 0 1012.2 10
10 0 1011.9 10
Weather
Flights
flights = mongo( collection = "Flights",db = "flights", url = "mongodb://localhost")
flights$drop()[1] TRUEflights$insert(flightdfF)
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Complete! Processed total of 336776 rows.$nInserted
[1] 336776
$nMatched
[1] 0
$nRemoved
[1] 0
$nUpserted
[1] 0
$writeErrors
list()flights$count()[1] 336776flights$find(limit = 10)
Found 10 records...
Imported 10 records. Simplifying into dataframe... date dep_time dep_delay arr_time arr_delay carrier tailnum flight
1 1/1/2013 517 2 830 11 UA N14228 1545
2 1/1/2013 533 4 850 20 UA N24211 1714
3 1/1/2013 542 2 923 33 AA N619AA 1141
4 1/1/2013 544 -1 1004 -18 B6 N804JB 725
5 1/1/2013 554 -6 812 -25 DL N668DN 461
6 1/1/2013 554 -4 740 12 UA N39463 1696
7 1/1/2013 555 -5 913 19 B6 N516JB 507
8 1/1/2013 557 -3 709 -14 EV N829AS 5708
9 1/1/2013 557 -3 838 -8 B6 N593JB 79
10 1/1/2013 558 -2 753 8 AA N3ALAA 301
origin dest air_time distance hour minute
1 EWR IAH 227 1400 5 17
2 LGA IAH 227 1416 5 33
3 JFK MIA 160 1089 5 42
4 JFK BQN 183 1576 5 44
5 LGA ATL 116 762 6 54
6 EWR ORD 150 719 6 54
7 EWR FLL 158 1065 6 55
8 LGA IAD 53 229 6 57
9 JFK MCO 140 944 6 57
10 LGA ORD 138 733 6 58
Flights
Planes
planes = mongo( collection = "Planes",db = "flights", url = "mongodb://localhost")
planes$drop()[1] TRUEplanes$insert(planesdf)
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Complete! Processed total of 3322 rows.$nInserted
[1] 3322
$nMatched
[1] 0
$nRemoved
[1] 0
$nUpserted
[1] 0
$writeErrors
list()planes$count()[1] 3322planes$find(limit = 10)
Found 10 records...
Imported 10 records. Simplifying into dataframe... tailnum year type manufacturer model engines
1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR 2
2 N102UW 1998 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
3 N103US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
4 N104UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
5 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR 2
6 N105UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
7 N107US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
8 N108UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
9 N109UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
10 N110UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214 2
seats engine
1 55 Turbo-fan
2 182 Turbo-fan
3 182 Turbo-fan
4 182 Turbo-fan
5 55 Turbo-fan
6 182 Turbo-fan
7 182 Turbo-fan
8 182 Turbo-fan
9 182 Turbo-fan
10 182 Turbo-fan
Planes
Airports
airports = mongo( collection = "Airports",db = "flights", url = "mongodb://localhost")
airports$drop()[1] TRUEairports$insert(airportsdf)
Processed 1000 rows...
Complete! Processed total of 1397 rows.$nInserted
[1] 1397
$nMatched
[1] 0
$nRemoved
[1] 0
$nUpserted
[1] 0
$writeErrors
list()airports$count()[1] 1397airports$find(limit = 10)
Found 10 records...
Imported 10 records. Simplifying into dataframe... faa name lat lon alt tz dst
1 04G Lansdowne Airport 41.13047 -80.61958 1044 -5 A
2 06A Moton Field Municipal Airport 32.46057 -85.68003 264 -5 A
3 06C Schaumburg Regional 41.98934 -88.10124 801 -6 A
4 06N Randall Airport 41.43191 -74.39156 523 -5 A
5 09J Jekyll Island Airport 31.07447 -81.42778 11 -4 A
6 0A9 Elizabethton Municipal Airport 36.37122 -82.17342 1593 -4 A
7 0G6 Williams County Airport 41.46731 -84.50678 730 -5 A
8 0G7 Finger Lakes Regional Airport 42.88356 -76.78123 492 -5 A
9 0P2 Shoestring Aviation Airfield 39.79482 -76.64719 1000 -5 U
10 0S9 Jefferson County Intl 48.05381 -122.81064 108 -8 A
Airports
AirLines
airlines = mongo( collection = "AirLines",db = "flights", url = "mongodb://localhost")
airlines$drop()[1] TRUEairlines$insert(airlinesdf)
Complete! Processed total of 16 rows.$nInserted
[1] 16
$nMatched
[1] 0
$nRemoved
[1] 0
$nUpserted
[1] 0
$writeErrors
list()airlines$count()[1] 16airlines$find(limit = 10)
Found 10 records...
Imported 10 records. Simplifying into dataframe... carrier name
1 9E Endeavor Air Inc.\r
2 AA American Airlines Inc.\r
3 AS Alaska Airlines Inc.\r
4 B6 JetBlue Airways\r
5 DL Delta Air Lines Inc.\r
6 EV ExpressJet Airlines Inc.\r
7 F9 Frontier Airlines Inc.\r
8 FL AirTran Airways Corporation\r
9 HA Hawaiian Airlines Inc.\r
10 MQ Envoy Air\r
AirLines
Conclusion
SQL and NOSQL databases each have their own strengths and weakness, and should be implemented according to the business scenario. Using Mongobd was interesting and fun endeavor. I encountered problems during the installation and creation phase.The Mongodb package used was mongolite due to the incompatibility of rmongodb with my R version. In the future I wish to study the query syntax deeper and perform left outer joins using $lookup to simulate SQL.
References
https://www.techwalla.com/articles/advantages-disadvantages-of-microsoft-sql
https://www.mongodb.com/scale/advantages-of-nosql
http://www.thegeekstuff.com/2014/01/sql-vs-nosql-db/?utm_source=tuicool
http://www.hadoop360.com/blog/advantages-and-disadvantages-of-nosql-databases-what-you-should-k
https://cran.r-project.org/web/packages/mongolite/mongolite.pdf
https://cran.r-project.org/web/packages/mongolite/vignettes/intro.html
---
title: "PROJECT 4 - NoSQL migration "
output: 
  html_notebook:
    theme: cosmo
    toc: true
    toc_float: true
    code_folding: show
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library("getPass")
library("RMySQL")
library("tidyr")
library("mongolite")


```


#Advantages 

##NoSQL 

###Database can handle Large volumes of structured, semi-structured, and unstructured data. 
###The database is structure for Speed. 
###No SQL databases can be scaled by adding non commonly used servers. 
###Dynamic Schema ensures the flexibility to add new fields for data.

##SQL

###SQL databases have a predefine Schema.
###Each table is structured and defined.
###Acid compliant (Atomicity, Consistency, Isolation, Durability)
###Reliable support by vendor.

#Disadvantages

##NOSQL

###NO SQL databases have been around for a short period of time.
###Support can be limited to the NO SQL community.
###Not Acid compliant.


##SQL

###Vendor pricing can be very high.
###Database may be compatible to specific hardware
##Limited compatibility such as running on windows based servers instead of UNIX.


#Migration
I first start by connecting to MySQL database in my local computer. The database i will be migrating is the Flights database given at the beginning of the course.


![Flight Schema](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/FlightSchema.JPG)


After connecting to the local database I created a separate query for each table. Using Tidyr I modified the dates columns in the flightsdf and weatherdf data frame to a standard date format. Packages used in the below step are RMYSQL and Tidyr.
```{r cars}

 # set MySQL information
Yuser = 'root'
Ydbname ='flights'
Yhost = 'localhost'
# create db connection with password promt
mydb = dbConnect(MySQL(), user= Yuser, password=getPass('Enter Database Password'), dbname=Ydbname, host=Yhost)
# query for the view
planesq = dbSendQuery(mydb,"select * from planes;")

# place query values in data frame
planesdf = data.frame( fetch(planesq, n=-1))

head(planesdf,5)

flightsq = dbSendQuery(mydb,"select * from flights;")
# place query values in data frame
flightsdf = data.frame( fetch(flightsq, n=-1))

head(flightsdf,5)

airportsq = dbSendQuery(mydb,"select * from airports;")
# place query values in data frame
airportsdf = data.frame( fetch(airportsq, n=-1))

head(airportsdf,5)

weatherq = dbSendQuery(mydb,"select * from weather;")
# place query values in data frame
weatherdf = data.frame( fetch(weatherq, n=-1))

head(weatherdf,5)

airlinesq = dbSendQuery(mydb,"select * from airlines;")
# place query values in data frame
airlinesdf = data.frame( fetch(airlinesq, n=-1))

head(airlinesdf,5)

# close database connection
dbDisconnect(mydb)

flightdfF = unite( flightsdf, "date", month,day,year, sep = "/")
head(flightdfF,5)

weatherdfF =  unite(weatherdf, "date", month,day,year, sep = "/")
head(weatherdfF,5)

```


#Mongodb
After modifying the data I created a separate connection for each data frame to be inserted into a collection/table.The code below will always drop the collection upon execution, insert the corresponding data frame, count the documents/rows, and display to documents.
![Terminology](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/Terminology.JPG)


#Weather
```{r , warning=FALSE,message=FALSE}

weather = mongo(collection = "Weather" ,db = "flights", url = "mongodb://localhost")
weather$drop()
weather$insert(weatherdfF)
weather$count()
weather$find(limit = 10)
```
![Weather](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/Weather.JPG)



#Flights

```{r, message=FALSE, warning=FALSE}

flights = mongo( collection = "Flights",db = "flights", url = "mongodb://localhost")
flights$drop()
flights$insert(flightdfF)
flights$count()
flights$find(limit = 10)
```
![Flights](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/Flights.JPG)



#Planes

```{r, message=FALSE, warning=FALSE}

planes = mongo( collection = "Planes",db = "flights", url = "mongodb://localhost")
planes$drop()
planes$insert(planesdf)
planes$count()
planes$find(limit = 10)



```
![Planes](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/Planes.JPG)



#Airports

```{r, message=FALSE, warning=FALSE}

airports = mongo( collection = "Airports",db = "flights", url = "mongodb://localhost")
airports$drop()
airports$insert(airportsdf)
airports$count()
airports$find(limit = 10)



```
![Airports](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/Airports.JPG)



#AirLines

```{r, message=FALSE, warning=FALSE}

airlines = mongo( collection = "AirLines",db = "flights", url = "mongodb://localhost")
airlines$drop()
airlines$insert(airlinesdf)
airlines$count()
airlines$find(limit = 10)


```
![AirLines](/Users/OmegaCel/Documents/MasterDataAnalytics/IS607DataAcquisition&Management/Project4/AirLines.JPG)


#Conclusion
SQL and NOSQL databases each have their own strengths and weakness, and should be implemented according to the business scenario. Using Mongobd was interesting and fun endeavor. I encountered problems during the installation and creation phase.The Mongodb package used was mongolite due to the incompatibility of rmongodb with my R version. In the future I wish to study the query syntax deeper and perform left outer joins using $lookup to simulate SQL.


#References

https://www.techwalla.com/articles/advantages-disadvantages-of-microsoft-sql

https://www.mongodb.com/scale/advantages-of-nosql

http://www.thegeekstuff.com/2014/01/sql-vs-nosql-db/?utm_source=tuicool

http://www.hadoop360.com/blog/advantages-and-disadvantages-of-nosql-databases-what-you-should-k

https://cran.r-project.org/web/packages/mongolite/mongolite.pdf

https://cran.r-project.org/web/packages/mongolite/vignettes/intro.html
